Authors: Taiwo Ayodele Aderinola Uche Capulet Anyaiwe Sinmiloluwa Esther Ogunbowale
This study evaluates the potential of composite flours made from cassava, sorghum residue, and garden egg (both ripe and unripe) to create a nutritionally enhanced doughmeal. Seven flour blends were formulated: A (100% cassava), B (95% cassava, 2.5% sorghum residue, 2.5% ripened garden egg), C (95% cassava, 2.5% sorghum residue, 2.5% unripened garden egg), D (90% cassava, 5% sorghum residue, 5% ripened garden egg), E (90% cassava, 5% sorghum residue, 5% unripened garden egg), F (85% cassava, 7.5% sorghum residue, 7.5% ripened garden egg), and G (85% cassava, 7.5% sorghum residue, 7.5% unripened garden egg). Results showed that supplementation with sorghum residue and garden egg increased moisture (64.20-69.12%), protein (0.47-13.77%), fibre (0.95-2.03%), and ash (0.95-2.09%) contents, while reducing carbohydrate content (11.98-32.77%) compared to the control (A). Sample E had the highest moisture content (69.12%), while B and D, with ripened garden egg, showed higher protein content. Mineral analysis revealed that supplementation increased essential minerals, with D showing the highest values for calcium (15.09-77.25 mg/100 g), magnesium (35.27-92.01 mg/100 g), and potassium (42.02-62.49 mg/100 g). Phytochemical analysis showed higher total phenol (0.603-6.19 mg GAE/g) and flavonoid (0.026-0.027 mg RE/g) contents in samples with ripened garden egg. Samples with higher sorghum residue and ripened garden egg showed significant inhibition of α-amylase (10.77-54.87%) and α-glucosidase (7.05-67.95%), which may help manage postprandial glucose levels. Sensory evaluation revealed that while the control sample was preferred, samples with ripened garden egg were more acceptable than those with unripened garden egg, indicating their potential for developing nutritionally improved cassava-based doughmeals. ]]>TRANSPLANTATION 10.21926/obm.transplant.2603277
Authors: Anastasia Doroshenko Matthias Reimers Gary Sawers Gerald Schlaf
Donor-specific antibodies (DSA) directed against human leukocyte antigens (HLA) are regarded as the principal cause of graft failure, including allograft loss. Especially cytotoxic, i.e., complement-fixing, antibodies have long been classified as clinically highly deleterious in the context of graft failure. The complement-dependent cytotoxicity assay (CDC), performed in cell-tray analyses, has been used to define these antibodies for more than 40 years, but generally lacks sensitivity. Due to its limited resolution, which does not exceed the single-donor level, it also cannot specify distinct antigens, especially in highly immunized patients. About fifteen years ago, this problem was overcome by broadly established Luminex-based single-antigen specification assays (SAB), which identify antibodies directed against recombinant single HLA antigens. To specify complement-fixing antibodies of postulated higher clinical relevance, these assays were supplemented by two commercial kits, initially by the C1q assay and later by the C3d assay. Here, we used the C3d assay to investigate serum from 74 selected patients on kidney waiting lists, which represent three groups, each with increasing levels of immunization/panel-reactive antibodies (%PRA): (i) with only anti-HLA class I antibodies (n = 24); (ii) with only anti-HLA class II antibodies (n = 21); and (iii) with combined anti-HLA class I and class II antibodies (n = 29). Our data show that C3d assay-defined cytotoxicity, especially for anti-HLA class I antibodies, strongly correlates with the mean fluorescence intensity (MFI). Anti-HLA class I antibodies appear cytotoxic when corresponding SAB MFI values exceed 6,000 to 7,000, whereas below this threshold cytotoxicity does not appear. Dilution experiments showed that cytotoxic antibodies could be converted to non-cytotoxic antibodies simply by falling below this threshold MFI value. In contrast, anti-HLA class II antibodies with cytotoxicity-defining threshold MFI-values between 1,500 and 2,000 differ by having a higher proportion of non-cytotoxic antibodies that even lie above these MFI-values. Nevertheless, we conclude that due to strong correlations with SAB MFI-values (Pearson correlation coefficients of 0.69 for HLA-class I and 0.63 for HLA-class II), there is insufficient clinical applicability to justify the considerable amount of work involved, and especially the high associated costs, for routine use of the C3d assay. ]]>GENETICS 10.21926/obm.genet.2603359
Authors: Syahrul Tuba Luluk Sakdiyyah Natanael Panjaitan F. Josse Pasca Pradana Adi Priyono Budi Sumaryono
Tamoxifen remains a cornerstone in the treatment of estrogen receptor-positive (ER+) breast cancer; however, variability in patient response underscores the necessity of a pharmacogenomic approach. This review integrates germline and somatic pharmacogenomics to elucidate the molecular determinants of tamoxifen metabolism, efficacy, and resistance. Germline polymorphisms, particularly in CYP2D6, CYP3A4/5, UGT2B7, and SULT1A1, significantly influence endoxifen levels, driving therapeutic variability across ethnic populations. Moreover, somatic mutations, notably in ESR1, TP53, and enhancer regions, confer acquired resistance and affect treatment outcomes. Ethnicity-specific distributions of these variants further highlight the inadequacy of a one-size-fits-all dosing strategy. Although CPIC and DPWG guidelines focus on CYP2D6 genotyping, they do not incorporate critical somatic data and phase II metabolism. To achieve truly personalized tamoxifen therapy, clinical algorithms must evolve to integrate germline and somatic profiles, enabling predictive, population-aware, and mutation-guided endocrine treatment. This review advocates for expanded multi-omics frameworks and multiethnic databases to optimize tamoxifen use and overcome endocrine resistance globally. ]]>Authors: Taiwo Ayodele Aderinola Uche Capulet Anyaiwe Blessing Oluwanifemi Adeyemi
Yam is a widely consumed staple food but has a high carbohydrate content and limited functional attributes. This study evaluated the nutritional, antioxidant, and in vitro antidiabetic properties of doughmeals produced from yam and tigernut flour blends at ratios of 95:5, 90:10, 85:15, 80:20, and 100% yam (control). Results showed that tigernut inclusion significantly improved the nutritional profile, with protein increasing from 8.09% to 11.81% and crude fiber rising from 0.24% to 1.14%. Ash and fat contents also increased notably, reflecting the mineral-dense and lipid-rich nature of tigernut. Phytochemical analysis revealed a substantial boost in total phenolic content, peaking at 14.07 mg GAE/g in the 20% blend compared with 6.56 mg GAE/g in the control. These compositional changes improved the functional and antioxidant potential of the doughmeals. Antioxidant capacity improved significantly across all assays, with hydroxyl radical scavenging nearly doubling (37.73% to 74.15%) and ABTS activity reaching 88.69%. Critically, the blends demonstrated significant in vitro inhibitory activity against key carbohydrate-digesting enzymes. Alpha-amylase inhibition rose from 43.74% to 80.61%, while alpha-glucosidase inhibition reached 72.56% in the fortified samples. Sensory evaluation confirmed that these improvements did not compromise consumer appeal, as overall acceptability remained statistically comparable to the traditional control. The findings indicate that tigernut incorporation, up to 20%, enhanced the nutritional composition, antioxidant capacity, and in vitro carbohydrate-digesting enzyme inhibitory potential of the doughmeals. ]]>GENETICS 10.21926/obm.genet.2603358
Authors: Heba Salman Mahdi Alabayji Ahlam Kadhim Naeem
Klebsiella pneumoniae is an opportunistic pathogen responsible for a wide range of healthcare-associated infections. It is frequently linked to resistance to multiple antibiotic classes and can persist in hospital environments despite infection control measures. This study aimed to evaluate the distribution of antibiotic resistance genes among K. pneumoniae. A total of 38 isolates were obtained from different clinical specimens and identified using the VITEK 2 Compact (GN-ID Card) and PCR targeting 16S rRNA. Multiple antibiotic-resistant isolates were characterized using the disc diffusion method. Resistance genes were detected by PCR, including blaTEM, blaSHV, blaPER, ctx-M1, ctx-M9, aph(3′)-Ia, aac(3′)-Ia, aac(3′)-IIa, aac(3′)-Iva, aac(6′)-Ib, ant(2′′)-Ia, ant(4′)-IIa. Results revealed a wide distribution of resistance genes. Among β-lactamase genes, blaTEM was predominant, detected in 32 isolates (84.2%), compared with blaSHV in 21 isolates (55.3%), while blaPER was absent. Cephalosporin resistance genes included ctx-M1, ctx-M9 and aph(3′)-Ia, which were detected in 15 (39.5%), 8 (21.1%), and 10 (26.3%) isolates, respectively. Aminoglycoside resistance genes were also prevalent: aac(3′)-Ia (76.3%), aac(3′)-IIa (44.7%), and aac(3′)-Iva (36.8%). Other genes included ant(2′′)-Ia (42.1%), ant(4′)-IIa (15.8%), and aac(6′)-Ib (10.5%) were detected. The high prevalence of multidrug resistance highlights the correlation between phenotypic and genotypic traits. Resistance may also be associated with phenotypes such as capsule production or biofilm formation, rather than solely with resistance genes. ]]>NEUROBIOLOGY 10.21926/obm.neurobiol.2603353
Authors: Kawther Al-Hadidi Jafar Alshraiedeh Mohammad Saleh
Postoperative delirium (POD) constitutes a frequent but severe complication that develops after patients undergo coronary artery bypass graft (CABG) operations. The condition results in extended stays in intensive care units, together with functional deterioration, higher medical expenses, and increased death rates. This review analyzes preoperative orientation programs based on implementation evidence demonstrating their effectiveness in reducing postoperative delirium rates among adult CABG patients. The research team conducted a scoping review using JBI methodology and PRISMA-ScR guidelines. The research team conducted database searches that covered the time period from 2000 through December 31, 2025. The research team included studies that involved adult CABG patients who received orientation programs and used validated assessment tools to measure delirium. The review identified four studies, including three RCTs and one observational study. The studies showed that multiple intervention types reduced delirium symptoms among patients. The interventions included multimedia content together with nurse-led ICU orientation programs and digital cognitive training orientation sessions. The results demonstrate that preoperative orientation serves as an effective low-risk method that hospitals can use to prevent postoperative delirium. The research team requires larger trials to test their hypothesis. ]]>TRANSPLANTATION 10.21926/obm.transplant.2603276
Authors: Elham Amjad Babak Sokouti
After liver transplantation, colorectal cancer (CRC) recurrence is a serious clinical issue. This underscores the need to understand the molecular and immunometabolic axes that promote tumor development and transplant fragility. This research combines compound-gene network analysis, immune cell deconvolution, and transcriptome profiling to find important genes and pathways that connect post-transplant immune modulation and CRC development. ADH1B, ALB, and ZG16 are consistently altered in primary colorectal malignancies, metastatic liver lesions, and transplanted hepatic tissue. These genes are involved in ethanol and retinol metabolism, systemic inflammation, immune cell infiltration, and preservation of the mucosal barrier. Our research shows that these genes are at the center of a common immunometabolic axis that lets the body change its metabolism and evade the immune system. Curcumin, beta-carotene, resveratrol, and D-mannitol are some of the bioactive dietary compounds that we find target these genes and related pathways. These could improve clinical outcomes in liver transplantation and colorectal cancer by changing how the immune system works. In addition to recommending ADH1B and ALB as potential biomarkers for recurrence monitoring and therapeutic targeting, this work offers a unified model of tumor-host-graft interactions. These findings lay the groundwork for future investigations that combine single-cell and spatial analysis to better understand gene function and direct precision therapies in transplant medicine and cancer, and are particularly relevant to patients undergoing liver transplantation for unresectable colorectal liver metastases (CRLM). ]]>Authors: Ayumu Hirayama Mao Narita Shota Ito Daisuke Nakane Shimaa Hosny Takashiro Akitsu
In this study, we assessed the reliability and limitations of density functional theory (DFT) and time-dependent density functional theory (TD-DFT) in predicting the spectroscopic and electrochemical properties of four azo-amino acid Schiff-base Cu(II) complexes by comparing calculated ultraviolet-visible (UV-vis) absorption and circular dichroism (CD) spectra and reduction potentials with experimental data obtained from spectroscopic measurements and cyclic voltammetry (CV). The optimized coordination geometries were generally close to square planar, whereas the phenylalanine complex showed the largest distortion, with a τ4 value of 0.204. TD-DFT reproduced some major features of the experimental UV-vis and CD spectra qualitatively; however, the calculated UV-vis bands were generally shifted toward shorter wavelengths, whereas the CD spectra showed discrepancies in wavelength, intensity, and sign. In particular, the positive experimental CD features in the approximately 300-325 nm region were predicted with the opposite sign for all four complexes. For the Cu(II)/Cu(I) couples, the calculated reduction potentials differed by 0.21-0.32 V between the two computational protocols, and the predicted ordering of reducibility also changed between the two protocols. Moreover, this ordering was inconsistent with the trend inferred from the lowest unoccupied molecular orbital energies. These results demonstrate that agreement in spectral peak positions or frontier-orbital trends alone is insufficient to establish the quantitative reliability of DFT and TD-DFT predictions for transition-metal complexes. Property-specific benchmarking and careful treatment of structural relaxation, solvation, and possible conformational variability are therefore required when applying these methods to such systems in solution. ]]>Authors: Attidiyage Don Shashika Iresh Bandunee C.L. Athapattu W.C.D. Kumari Fernando Jayantha T.B. Obeysekera Upaka Rathnayake
This study developed future regional Intensity-Duration-Frequency curves for Sri Lanka using climate change scenarios to address the growing need for adaptive infrastructure planning. Rainfall projections were generated with the CNRM-CM6-1 global climate model under Shared Socioeconomic Pathways (SSP) for moderate (SSP2-4.5) and high (SSP5-8.5) emissions. These projections were bias-corrected using a linear scaling method and analyzed across eleven hydro-meteorologically homogeneous regions. IDF curves were constructed for three future periods: near future (2025-2049), middle future (2050-2074), and far future (2075-2099). The results demonstrate significant spatial and temporal variability in rainfall intensity. Change factors were estimated for all regions to quantify deviations from historical baselines, identifying regions 5, 7, and 9 as particularly vulnerable to climate change under both SSPs. For SSP2-4.5, the overall change factor values are 1.73 for the near future, 1.65 for the middle future, and 1.335 for the far future, with an average of 1.57 across all periods. For SSP5-8.5, the values are 1.72 for the near future, 1.70 for the middle future, and 1.82 for the far future, resulting in an average of 1.74. The findings indicate a trend toward more extreme rainfall under both scenarios, with greater intensity and uncertainty under SSP5-8.5. These results underscore the urgent need for region-specific adaptation strategies, improved flood resilience, and the integration of climate projections into national infrastructure planning and disaster risk management. ]]>Authors: Adeeb Zaid Husam Al-Ziyadi Jacob Wekalao Prasenjit Maity
Photocatalytic degradation of organic dyes using semiconductor materials has been widely investigated for wastewater treatment. Yet, high activity reported under controlled laboratory conditions does not necessarily translate into practical treatment performance. This review evaluates the hypothesis that rare-earth (RE) doping can improve metal-oxide photocatalysis and examines whether the reported improvements are supported by adequate mechanistic evidence and relevant practical validation. A structured literature search of Web of Science and Scopus identified 29 studies of RE-doped metal oxides for dye degradation. The analysis considered catalyst composition, dopant concentration, morphology, synthesis route, operating conditions, photocatalytic kinetics, mechanistic characterization, mineralization, stability, leaching, and real-wastewater testing. Reported removal efficiencies ranged from 77 to 100%, with a mean of 92.9%; however, these values were not used to rank catalysts because experimental conditions differed substantially. Mechanistic evidence was classified as direct in 15 studies (51.7%) and partial in 14 (48.3%). PL and XPS were widely reported, whereas EIS and ESR/EPR were used less frequently, indicating that proposed charge-transfer and reactive-species mechanisms are not consistently supported by complementary evidence. TOC analysis was reported in 10 studies (34.5%), none reported apparent quantum yields, and none tested RE-doped photocatalysts with real wastewater. Stability or reuse testing was reported in 20 studies (69.0%), while quantitative rare-earth leaching was reported in only one study (3.4%). These findings show that RE doping can modify photocatalyst properties, but its practical value cannot be established from dye-removal efficiency alone. Standardized reporting, mechanistic validation, real-wastewater testing, long-term stability and leaching assessment, and pilot-scale evaluation are required to advance RE-doped photocatalysts toward reliable wastewater treatment. ]]>Authors: María E. Raygoza-Limón Gabriel Trujillo-Hernández José Alejandro Amezquita Garcia Abelardo Mercado-Herrera Juan Carlos Ling-López Fabian N. Murrieta-Rico
This study evaluates the carbon-related performance of grid-connected photovoltaic (PV) systems under different electricity-generation mixes using a literature-based, scenario-oriented life-cycle GHG emissions assessment. A 1 MWp utility-scale crystalline-silicon PV reference system was defined with an annual electricity generation of 1,640 MWh, a 30-year lifetime, and an annual degradation rate of 0.5%, resulting in a projected cumulative electricity output of 45,793.99 MWh. The analysis considered three benchmark electricity-mix scenarios: a high-carbon grid, a transitioning grid, and a low-carbon benchmark grid. The gross life-cycle GHG burden of the PV system was interpreted using a literature-based range of 10-36 g CO2e/kWh, with 19 g CO2e/kWh adopted as the median reference case. Results show that scenario-estimated avoided emissions and net GHG benefits are highest in carbon-intensive electricity systems and decrease progressively as grid carbon intensity declines. Nevertheless, the net GHG benefit remains positive across all benchmark scenarios and across the tested PV emission-factor range. The findings indicate that PV deployment provides the greatest immediate GHG mitigation in fossil-intensive grids, while long-term improvements depend on cleaner manufacturing, low-carbon supply chains, circular economy strategies, and responsible end-of-life management. Because the analysis relies on literature-derived PV emission factors and average grid carbon-intensity benchmarks, the results should be interpreted as comparative scenario estimates rather than as outputs from a full process-based LCA model. ]]>TRANSPLANTATION 10.21926/obm.transplant.2603275
Authors: Kathryn L White Carol Crochet Maheswaran Pitchaimuthu
Outcomes in liver transplantation remain constrained by challenges in donor selection, organ allocation, and long-term graft survival, and traditional scoring systems such as MELD, SOFT, and the Donor Risk Index have well-recognized limitations in capturing the nonlinear complexity of transplant data. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offers analytical methods capable of processing high-dimensional data beyond conventional regression, but the maturity and clinical readiness of this evidence base across the liver transplant continuum have not been comprehensively characterized. A narrative review was conducted via systematic search of PubMed, MEDLINE, and the Cochrane Library (January 1991-June 2025) using combinations of terms including “artificial intelligence,” “machine learning,” “deep learning,” “liver transplant,” “organ allocation,” and “graft failure.” Of 754 records identified, 45 studies met inclusion criteria following independent dual-reviewer screening. Each study was classified by evidence level as exploratory (internal validation only), externally validated, or implementation-oriented, and organized according to the pre-transplant and post-transplant continuum. AI applications span transplant candidacy assessment, organ allocation and waitlist prioritization, donor-recipient matching, graft quality assessment, and post-transplant prediction of acute kidney injury, sepsis, cardiovascular events, graft dysfunction, biliary complications, hepatocellular carcinoma (HCC) recurrence, immunosuppression dosing, and mortality. Performance varied widely (AUC/C-index 0.63-0.99), with the most methodologically mature evidence found for HCC recurrence prediction (C-index 0.75-0.839, including one internationally validated model) and waitlist mortality modeling. Notably, logistic regression outperformed several ML algorithms in the largest donor-recipient matching registry analyzed (n > 39,000), and cross-national validation of mortality models revealed substantial performance degradation (AUROC 0.70-0.74) when applied across healthcare systems. Fewer than half of included studies used explainability methods such as SHAP, and only two models explicitly incorporated equity considerations into their design. AI is increasingly applied across the liver transplant continuum and shows particular promise for HCC recurrence prediction and waitlist risk stratification. However, the field is limited by a predominance of single-center exploratory studies with internal validation only, inadequate attention to algorithmic fairness and bias, and an absence of prospective evaluation or regulatory engagement-no AI model in liver transplantation has yet received regulatory approval for clinical use. Future progress will require standardized data frameworks, multicenter prospective validation, formal reporting guidelines, and equity auditing before AI tools can be considered genuinely practice-changing. ]]>Authors: Wilson C. Chin
Knowledge of permeability, which measures a rock formation’s resistance to fluid flow, is essential to energy exploration, oilfield development and economic viability, since its value directly relates to cash flow and outlay: the greater permeability is, the faster monetary gains are realized, the lower the initial investment required. Formation testing is crucial to this understanding. It assists with reservoir characterization as it enables direct fluid sampling, reducing the need for resistivity, acoustic and nuclear data, and also because pressure changes measured during pumping can be analyzed with Darcy flow models to predict permeability and pore pressure. The author invented Halliburton’s real-time 1997 GeoTap™ model in U.S. Patent 5,703,286 for an isotropic permeability “k,” one which assumes uniform initial pressure conditions. However, real world environments include overbalanced boreholes whose “supercharged, invading” flows strongly affect events at the anisotropic sandface. In practice, dual packers are used to improve predictions by isolating local zones from nearby high pressure surroundings, thus reducing contamination and enabling deeper transient analysis. Within these zones, pressures are high but constant, providing a key entry point for math modeling. This paper extends the 1997 model, so that a newer exact, closed form, analytical “forward” method is available to predict pressure fields when formation parameters are inputted, and which improves “inverse models” to help predict permeability and pore pressure under highly supercharged conditions when (pressure, time) pairs are known at the source probe, also demonstrating how synthetic data from forward analysis can be used to validate overall workflow and inverse development strategies. How the new approximate models relate to recent, more complete 2024 azimuthal multiprobe models requiring substantial computation is also explained, with the aim of facilitating drilling and well logging operations and more effective reservoir characterization. The present work provides a rapid and convenient platform for field use, just as the 1997 GeoTap™ model simplified both math and physics for use during initial logging when fewer input modeling parameters are available. We additionally review our recent 2024 azimuthal multiprobe methods which fully account for dynamic invasion, transient mudcake growth and multiphase effects, which again complement the approximate tools developed here to estimate permeability. Quantifying these effects with the approximate model allows oil companies to rapidly formulate drilling and production programs, leaving more complete analysis to detailed models only when satisfactory input data are available. The combined tools support cost effective processes in commercializing newly discovered resources, thus improving on an understanding essential to energy exploration, oilfield development and economic productivity. ]]>NEUROBIOLOGY 10.21926/obm.neurobiol.2603352
Authors: Ishak Pacal
Accurate brain tumor classification from magnetic resonance imaging (MRI) remains difficult because glioma, meningioma, and pituitary tumors may exhibit overlapping enhancement, morphology, and slice-dependent appearance. This study presents a controlled comparison of Vision Transformer, Data-efficient Image Transformer (DeiT), Swin Transformer, BEiT, and EVA-02 under a common image-level protocol. The public Epic and CSCR Hospital Dataset record describes 12,064 preprocessed T1-weighted contrast-enhanced MRI images and was evaluated according to its published partitioning scheme. The original test set was reserved for final evaluation, and a validation subset was drawn solely from the original training set. BEiT-Base produced the highest observed accuracy (0.9921; Wilson 95% confidence interval, 0.9877-0.9950), a macro F1-score of 0.9924, and a macro area under the receiver operating characteristic curve of 0.9995, at a profiled cost of 33.70 giga floating-point operations per image, equal to that of Vision Transformer-Base and DeiT-Base. Accuracy intervals overlapped across models, and the differences were interpreted descriptively. Gradient-weighted Class Activation Mapping showed activation near the visually apparent lesion in many cases, but peripheral and non-lesion responses were also observed. Because patient and examination identifiers are unavailable, the findings represent image-level benchmark performance and do not establish patient-level generalization. ]]>Authors: Kevin Liu Wenbiao LU Kaiyu Xiao
Trace minerals (Cu, Fe, Mn, Zn, I and Se) are essential components of numerous enzymes thus indispensable for metabolism, immunity, growth and reproduction. Inorganic forms of these minerals (ITM) are widely used in the poultry industry often with much in excess in order to cover various uncertainties, resulting in not only oxidative deterioration of feeds and gastro-intestinal health, but also large proportions to be excreted into the eco-system, leading to many jurisdictions to set up limits of Cu and Zn usages in feeds. This study investigated feasibility and extent of reduction of supplemental ITM in broiler nutrition, by using 400 newly hatched Ross 308 male chicks, randomly assigned to four treatments with ten floor pen replicates per treatment and 10 birds per pen, to determine the efficacy of encapsulating all six trace minerals into a single micro-granule (Metal Polysaccharides Complex, MPSC, MinCo®) supplemented on descending pattern, on growth performance, digestibility of these minerals and carcass traits of broilers. The control diet was supplemented with ITM at their conventional levels (mg/kg: Cu 15, Fe 40, Mn 100, Zn 100, Se 0.3, and I 1.0) for the entire 42-day of life cycle. Treatments 2-4 tested MPSC in three formulas on descending inclusions. Feed intake, weight gain, feed conversion ratio (FCR) and mortality were measured by phases. Concentrations of trace minerals in feces and apparent digestibility were measured on days 33-35 using titanium dioxide. Footpad dermatitis (FPD), wooden breast (WB), white striping (WS), and mineral contents in liver and breast were measured on day 43. Data were analyzed by analysis of variance (ANOVA). The results showed all birds reached expected live weight 3.2 kg by day 42 at FCR 1.55, no significant differences were observed among treatments up to day 35. During the finisher phase, the birds fed on MPSC showed improved growth rate and FCR compared with those fed on ITM (P < 0.05). FPD, WB and WS did not differ. Minerals in MPSC showed significantly higher apparent digestibility (P < 0.05) than ITM. Calculation on the intakes of trace minerals revealed that the birds fed on ITM consumed 1,255 mg of Cu, Fe, Mn, Zn in total, whilst those on MPSC reduced consumption to 491 mg (Formula 1), 264 mg (Formula 2) and 312 mg (Formula 3). It can be concluded that MPSC encapsulation can significantly increase digestibility and retention of trace minerals which enables reduction of trace minerals usage by approximately 70% without compromising performance and carcass traits. ]]>Authors: Wilson C. Chin
Understanding anisotropy in reservoir dynamics is essential to energy exploration, oilfield development and economic viability since this indicator describes the natural flow directions preferred by the underground fluid. Quantifying its effects allows oil companies to design drilling programs and production facilities that extract the greatest benefit from newly discovered resources. Formation testers are important because they provide direct clues on hydrocarbon properties and resistance to flow through fluid sampling. For example, in Formation Testing While Drilling or FTWD, “spherical” or “effective permeability” keff = kh2/3kv1/3 can be predicted from the author’s 1990s real-time GeoTap™ methods when source probe pressure transient data alone is available. In hydraulic fracturing, infill drilling, reservoir engineering and wellbore stability, knowledge of individual horizontal and vertical permeabilities kh and kv is preferred. This is possible when additional pressure data at a nearby axially displaced passive observation probe is available from dual probe instruments. New algorithms for both kh and kv are designed from first principles and solved analytically. In particular, a “forward simulator” (solving for pressure when permeability inputs are given) and “inverse simulators” (predicting permeability when (pressure, time) pairs are available at both sensors) are developed. Validation challenges arise related to prediction accuracy. In 2008, Halliburton completed sponsorship of a Doctoral Thesis at The University of Texas at Austin, in which extensive lab experiments validated this author’s keff model, also supported by Halliburton. However, such multi-year efforts are expensive and impractical. Because the basic inverse ideas had been successfully tested, this paper develops an alternative innovative approach in which synthetic pressures created by forward simulators with assumed permeabilities are used in inverse procedures which attempt to recover these same permeabilities from (pressure, time) pairs obtained at source and observation probe locations. Forward and inverse methods are governed by the same Darcy flow formulation, but are solved completely differently. Agreement provides strong evidence for physical and mathematical consistency behind the validation strategy. The state-of-the-art is reviewed and comprehensive examples are offered to demonstrate the versatility and practical use of the new technology. ]]>Authors: Ashraf Osman Bira Wafaei Baneh Masoud Rahmati Deiranloei Koosha Wafaei Baneh
This research addresses how BIM can help manage the construction of post-disaster settlements utilizing the MiC technique. First, previous research work in this field is reviewed. Then, the challenges explored through literature synthesis and prioritization of BIM usage in the management of MiC projects were identified based on the qualitative opinions of a group of experts. After that, an approach to achieve selected and potential applications of BIM in the management of temporary housing projects based on MiC is explained through a case study. Finally, measuring the effectiveness of BIM in managing construction projects in crisis conditions is done by organizing interviews with groups of experts. The results indicate that utilizing the BIM model in a prefab temporary housing project based on MiC effectively improves the 3D visualization and simulation of the project, creates better communication and coordination between designers and contractors, ensuring high quality for the product, helping in planning the assembly and management of the project, and supporting estimation and quantity surveying, and eliminating construction element interference. ]]>GENETICS 10.21926/obm.genet.2603357
Authors: Tekin Ahmet Serel Onur Ertunç Merve Meryem Kıran Esra Şengül Zeynep Ruken Hakkoymaz Eda Uysal Aydın
Tumor mutational burden (TMB) is a critical biomarker associated with the response to immunotherapy in prostate cancer. The heterogeneity of tumors can complicate the prediction of TMB, making reliable detection essential for effective treatment planning. Recent advancements in deep learning (DL) have facilitated the analysis of histopathological images, enabling better predictions of TMB. This study utilized the Cancer Genome Atlas (TCGA) cohort of prostate cancer patients, comprising 580 H&E-stained whole slide images (WSIs) and corresponding mutation data. We classified TMB into high (TMB-H) and low (TMB-L) groups based on a threshold of 0.9. Pre-trained deep learning models, specifically Inception V3 and VGG16, were employed to analyze the WSIs. The models were trained using a fivefold cross-validation approach, and various data preprocessing and augmentation techniques were applied to enhance model performance. The Inception V3 model demonstrated superior predictive performance, achieving a training accuracy of 0.77 and validation accuracy of 0.81, compared to the VGG16 model, which had a training accuracy of 0.68 and validation accuracy of 0.77. Both models effectively classified patients into TMB-H and TMB-L categories, with the Inception V3 model showing lower validation loss, indicating better generalization to unseen data. Our findings suggest that deep learning models, particularly Inception V3, can effectively predict TMB status in patients using histopathological images. This research highlights the potential for integrating DL techniques in clinical settings to enhance personalized treatment strategies for patients with varying TMB. ]]>GENETICS 10.21926/obm.genet.2603356
Authors: Md Arif Sakil Shagata Islam Shorna Maisha Rahman Tahmina Akter Prodipto Bishnu Angon Arpita Rani Roy Mohammed Arif Sadik Polash
Salinity and drought stresses induced by climate change pose critical threats to global food security, necessitating a comprehensive insight of plant adaptive mechanisms at the genomic level. This review brings together recent advances in identifying genes, regulatory networks, and evolutionary strategies underlying plant responses to osmotic stress. We discuss key transcription factor families (DREB, NAC, MYB, and WRKY), ion transporters (SOS pathway, NHX, and HKT), genes involved in osmolyte biosynthesis, and reactive oxygen species (ROS) scavenging systems. Recent genomic studies have revealed extensive expansions of gene families, neofunctionalization events, and convergent evolution across plant lineages. Multiomics integration has illuminated complex regulatory networks involving microRNAs, long noncoding RNAs (lncRNAs), and epigenetic modifications that fine-tune stress responses. We examine natural variation in stress tolerance, highlighting genomic signatures of selection in halophytes and xerophytes that provide insights for crop improvement. Pangenomic analyses revealed that significant structural variations and presence-absence variations contributed to stress adaptation. Finally, we discuss evolutionary trade-offs, the impact of domestication on stress resistance, and future directions for leveraging genomic knowledge through precision breeding, gene editing, and systems biology approaches to develop climate-resilient crops. ]]>Authors: Alejandro Valencia-Arias Jorge Eugenio Cabrejos Barriga Cesar Alberto García Espinoza
The presence of arsenic in groundwater poses a significant threat to public health, particularly in rural areas lacking access to treatment technologies. In Mórrope, concentrations exceeding regulatory limits have been detected, necessitating sustainable solutions. The objective of this study was to evaluate the efficiency of sugarcane bagasse as a biosorbent material for removing arsenic from contaminated waters in the Mórrope-Lambayeque district. The experimental design employed a 2 × 3 × 3 factorial design, encompassing three variables: adsorbent dosage (1 and 2 g/L), pH (5, 7, and 9), and initial arsenic concentration (2.5, 4.5, and 6.5 mg/L). The bagasse underwent a series of processing steps, including acid washing, drying, and sieving, and was applied in batch tests. The analysis encompassed removal efficiency, adsorption capacity, isotherm adjustment, and kinetics. The system attained a maximum removal rate of 27.43% with a biosorbent dosage of 2 g/L, pH 9, and an initial arsenic concentration of 2.5 mg/L. The Langmuir model (R2 = 0.9926) provided the most suitable description of the adsorption process, indicating the presence of a monolayer. The adsorption kinetics were best described by the pseudo-second-order model (R2 = 0.9727). However, this statistical fit was not interpreted as definitive evidence of an exclusively chemisorption-controlled mechanism, since surface interactions, external mass transfer, and intraparticle diffusion may contribute simultaneously to arsenic uptake. The results indicate that chemically modified sugarcane bagasse exhibits a measurable but limited capacity for arsenic adsorption. However, the maximum removal efficiency of 27.43% is insufficient for direct drinking-water treatment, and the material should not be considered a stand-alone remediation technology under the evaluated conditions. Further surface modification, process optimization, and integration with complementary treatment stages are required before practical implementation can be considered. Beyond its technical feasibility, this approach holds strong social relevance, as it promotes the use of locally available agricultural residues to improve water quality in vulnerable communities, fostering low-cost, community-managed, and environmentally sustainable solutions for safe water access. Although the removal efficiency remains moderate, future research should aim to enhance adsorption performance and evaluate the potential for large-scale implementation in rural water treatment systems. ]]>