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Object segmentation in images is typically spatial and focuses on the spatial coherence of pixels. Nanoparticles in electron microscopy images are also segmented frame by frame, with subsequent morphological analysis. However, morphological analysis is inherently sequential, and a temporal regularity is evident in the process. In this study, we extend the spatially focused morphological analysis by incorporating a fusion of hard and soft inductive bias from sequential machine learning techniques to account for temporal relationships. Previously, spiky Au nanoparticles (Au-SNPs) in electron microscopy images were analyzed, and their morphological properties were automatically generated using a hourglass convolutional neural network architecture. In this study, recurrent layers are integrated to capture the natural, sequential growth of the particles. The network is trained with a spike-focused loss function. Continuous segmentation of the images explores the regressive relationships among natural growth features, generating morphological statistics of the nanoparticles. This study comprehensively evaluates the proposed approach by comparing the results of segmentation and morphological properties analysis, demonstrating its superiority over earlier methods.

The clinical and methodological diversity observed in national and regional diabetes surveys, emphasized on the need of the weighted average prevalence of diabetes. To measure the pooled prevalence of type 2 diabetes mellitus in the adult population of Pakistan. The prospective cross-sectional studies reporting adult diabetes in Pakistan and published on any date were retrieved from PubMed, ScienceDirect and PakMediNet databases. In the meta-analysis, PRISMA guidelines were used for reporting; the AXIS tool for assessing quality and risk of bias within studies; I statistics for measuring heterogeneity between studies and subgroups; and Tableau Public 10.4 for geographic mapping of included studies. Using Meta-Analyst 3.13 βeta, overall and subgroup pooled estimates were measured by random effects model. The pooled sample of twelve studies included 42,051 adults (≥20 years) comprised of both sexes from urban and rural Pakistan. The pooled prevalence of diabetes was 13.7% (95% CI, 10.7-17.3). None of the twelve studies was of poor quality (<10 scores). Ten studies were published in ISI indexed journals, and nine of them were indexed for Medline. The level of heterogeneity observed across studies and between subgroups was moderate (<50%). The subgroup analysis revealed a higher pooled estimate of diabetes in males than in females (13.1 vs. 12.4%). It was also higher in urban than in rural patients (15.1 vs. 1.6%), and in HbA1c than in OGTT tests (23.9 vs. 14.4%). However, pooled estimates of the WHO and the ADA criteria were similar (13.8 vs. 13.5%). The prevalence of diabetes is on the rise in the adult population of Pakistan. The heterogeneity across studies observed in the meta-analysis suggested that the design of future diabetes surveys should be efficient and purposeful, and that valid tools and methods should be used to generate more precise data. Moreover, harmony between the stakeholders is much needed to seek a true picture of the diabetes burden in the country.

Background A variety of screening tools and criteria are used for the diagnosis of gestational diabetes mellitus (GDM). As a result, the prevalence rate of GDM varied from 4.41% to 57.90% among studies from Pakistan. Beside this disagreement, similar multi-centric studies, community surveys and pooled evidence were lacking from the country. Therefore, this first systematic review and meta-analysis aimed to measure the overall and subgroup pooled estimates of GDM and explore the methodological variations among studies for any inconsistency. Methods Using the PRISMA guidelines, seventy studies were identified from PubMed, ScienceDirect, Google Scholar and PakMediNet database. Of them, twenty-four relevant studies were considered for systematic review and nine eligible studies selected for meta-analysis. AXIS was used for measuring quality of reporting, I ^2 statistics for heterogeneity among studies and subgroups, funnel plot for reporting potential publication bias and forest plot for presenting pooled estimates. Results The pooled sample of nine studies was 27,034 (126 – 12,450) pregnant women, of any gestational age, from all four provinces of Pakistan. Overall pooled estimate of GDM was 16.7% (95% CI 13.1 – 21.1). The highest subgroup pooled estimate of GDM observed in studies from Balochistan (35.8%), followed by Islamabad (23.9%), Khyber Pakhtunkhwa (17.2%), Sindh (13.2%), and Punjab (11.4%). The studies that adopted 75g 2-h OGTT had a little lower pooled estimate (16.3% vs. 17.3%); and that adopted diagnostic cut-off values [≥ 92 (F), ≥ 180 (1-h) and ≥ 153 (2-h)] had a greater pooled estimate (25.4% vs. 15.8%). The studies that adopted Carpenter criteria demonstrated the highest subgroup pooled estimate of GDM (26.3%), after that IADPSG criteria (25.4%), and ADA criteria (23.9%). Conclusions Along with poor quality of reporting, publishing in non-indexed journals and significant disagreement between studies, the prevalence rate of GDM is high in Pakistan. Consensus building among stakeholders for recommended screening methods; and continuous medical education of the physicians are much needed for a timely detection and treatment of GDM.

Background In the past two decades, there has been a growing recognition of the need to establish indigenous standards or reference growth charts, particularly following the WHO multicenter growth study in 2006. The availability of accurate and reliable growth charts is crucial for monitoring child health. The choice of an appropriate model for constructing growth charts depends on various data characteristics, including the distribution’s tails and peak. While Pakistan has reported some reference growth charts, there is a notable absence of indigenous charts for children under two years of age, especially for infants aged 0–6 months who are exclusively breastfed. Additionally, acquiring data poses a significant challenge, particularly for low-income countries, as it demands substantial resources such as finances, time, and expertise. The Multiple Indicator Cluster Survey (MICS) constitutes a large-scale national survey conducted periodically in low-income countries under the auspices of UNICEF. In this study, we propose methods for generating selection variables utilizing the “Novel Case Selection Method,“ as previously published. Further our approach enables to select and fit appropriate model to the MICS data, selected, and to develop the standard growth charts. Methods Out of the 11,478 children under 6 months of age included in MICS-6 (Pakistan), 3,655 children (1,831 males and 1,824 females) met the specified criteria and were selected using the “Novel Case Selection Method”. The sample was distributed across provinces as follows: 841 (23.0%) from KPK, 1,464 (40.1%) from Punjab, 819 (22.4%) from Sindh, and 531 (14.5%) from Balochistan. This sample encompassed both rural (76.4%) and urban (23.6%) populations. Following data cleaning and outlier removal, a total of 3,540 records for weight (1,768 males and 1,772 females) and 3,515 records for height (1,759 males and 1,756 females) were ultimately available for the development of standard charts. The Bayesian Information Criterion (BIC) was employed to determine the optimal degrees of freedom for L, M, and S using RefCurv_0.4.2 . Three families within the gamlss class—namely, Box Cox Cole and Green (BCCG), Box Cox T (BCT), and Box Cox Power Exponential (BCPE)—were applied, each with three smoothing techniques: penalized splines (ps), cubic splines (cs), and polynomial splines (poly). The best-fitted model was selected from these nine combinations based on the Akaike Information Criteria. Results The Novel Case Selection Method yielded 3655 cases as per criteria. After cleaning the data, this method lead to selection of 3540 children for “weight for age” (W/A) and 3515 children for “height for age” (H/A). The “BCPE” family and “ps” as smoothing method proved to be best on AIC for all four curves, i.e. the W/A male, W/A female, H/A male, and H/A female. The optimum selected degrees of freedom for the curve “W/A”, for both genders were (M = 1, L = 0, S = 0). The optimum degrees of freedom for H/A male were again (M = 1, L = 0, S = 0), but for females the selected degrees of freedom were (M = 1, L = 1, S = 1). The indigenous fitted standard curves for Pakistan were on lower trajectory in comparison to WHO standards. Conclusion This study uses the Novel Case Selection Method with introduced algorithms to construct tailored growth charts for lower and middle-income countries. Leveraging extensive MICS data, the methodology ensures representative national samples. The resulting charts hold practical value and await validation from established data sources, offering valuable tools for policy makers and clinicians in diverse global contexts.

With the development of new technologies in recent years, researchers have made significant progress in crop breeding. Modern breeding differs from traditional breeding because of great changes in technical means and breeding concepts. Whereas traditional breeding initially focused on high yields, modern breeding focuses on breeding orientations based on different crops’ audiences or by-products. The process of modern breeding starts from the creation of material populations, which can be constructed by natural mutagenesis, chemical mutagenesis, physical mutagenesis transfer DNA (T-DNA), Tos17 (endogenous retrotransposon), etc. Then, gene function can be mined through QTL mapping, Bulked-segregant analysis (BSA), Genome-wide association studies (GWASs), RNA interference (RNAi), and gene editing. Then, at the transcriptional, post-transcriptional, and translational levels, the functions of genes are described in terms of post-translational aspects. This article mainly discusses the application of the above modern scientific and technological methods of breeding and the advantages and limitations of crop breeding and diversity. In particular, the development of gene editing technology has contributed to modern breeding research.

Multi-drug resistant bacteria sometimes known as “superbugs” developed through overuse and misuse of antibiotics are determined to be sensitive to small concentrations of silver nanoparticles. Various methods and sources are under investigation for the safe and efficient synthesis of silver nanoparticles having effective antibacterial activity even at low concentrations. We used a medicinal plant named Salvia moorcroftiana to extract phytochemicals with antibacterial, antioxidant, and reducing properties. Three types of solvents; from polar to nonpolar, i.e., water, dimethyl sulfoxide (DMSO), and hexane, were used to extract the plant as a whole and as well as in fractions. The biosynthesized silver nanoparticles in all extracts (except hexane-based extract) were spherical, smaller than 20 nm, polydispersed (PDI ranging between 0.2 and 0.5), and stable with repulsive force of action (average zeta value = -18.55±1.17). The tested bacterial strains i.e., Klebsiella pneumoniae , Pseudomonas aeruginosa , Staphylococcus aureus , and Enterococcus faecali s were found to be sensitive to even small concentrations of Ag-NPs, especially P . aeruginosa . The antibacterial effect of these Ag-NPs was associated with their ability to generate reactive oxygen species. DMSO (in fraction) could efficiently extract antibacterial phytochemicals and showed activity against MDR bacteria (inhibition zone = 11–12 mm). Thus, the antibacterial activity of fractionated DMSO extract was comparable to that of Ag-NPs because it contained phytochemicals having solid antibacterial potential. Furthermore, Ag-NPs synthesized from this extract owned superior antibacterial activity. However, whole aqueous extract-based Ag-NPs MIC was least (7–32 μg/mL) as compared to others.

Common bean ( Phaseoulus vulgarsis ) is an important edible legume crop, but its improvement through modern biotechnological tools has been limited due to the lack of efficient and reproducible in vitro regeneration protocols. This bottleneck restricts the application of biotechnological applications like genetic transformation, genome editing, and crop improvement. To address the challenges, an experiment was designed to investigate the combined effect of KNO 3 with Indole Butyric acid (IBA) and naphthalene acetic acid (NAA) on shoot proliferation using two different explants, followed by optimizing the results with quantum machine learning (QML) analysis. Results demonstrated enhanced shoot proliferation with increased KNO 3 levels (5700 mg/L), with a shoot count of 6.44 from the shoot meristem explant in the presence of NAA. Whereas lower KNO 3 concentration enlarged the shoot length. Results also revealed the impact of auxin-explant interaction, highlighting the significance of a tailored medium for in vitro regeneration. To strengthen prediction and optimization, classical and quantum machine learning (ML) algorithms were also used. A custom quantum circuit, utilizing RX, RZ, and Hadamard gates, was introduced to enhance classification accuracy using quantum superposition and entanglement principles. Results demonstrated the superior performance of a custom quantum circuit with an accuracy and F1 score of 83% and 84% respectively, for shoot counts. Whereas comparable performance was observed for shoot length classification. This hybrid computational approach not only reduced experimental uncertainty but also increased the nutrient-hormone optimization. These findings demonstrate the potential of hybrid quantum-classical approaches in plant biotechnology. The optimized in vitro system can facilitate downstream applications in plant biotechnology for breeding of legumes and other crops.

Green synthesis of nanoparticles is becoming a method of choice for biological research due to its environmentally benign outcomes, stability and ease of synthesis. In this study, silver nanoparticles (AgNPs) were synthesized using stem (S-AgNPs), root (R-AgNPs) and mixture of stem and root (RS-AgNPs) of Delphinium uncinatum . The synthesized nanoparticles were characterized by standardized techniques and evaluated for their antioxidant, enzyme inhibition, cytotoxic and antimicrobial potentials. The AgNPs exhibited efficient antioxidant activities and considerable enzyme inhibition potential against alpha amylase, acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) enzymes. S-AgNPs showed strong cytotoxicity against human hepato-cellular carcinoma cells (HepG2) and high enzyme inhibitory effect (IC50 values 27.5μg/ml for AChE and 22.60 μg/ml for BChE) compared to R-AgNPs and RS-AgNPs. RS-AgNPs showed significant inhibition of Klebsiella pneumoniae and Aspergillus flavus and exhibited higher biocompatibility (<2% hemolysis) in human red blood cells hemolytic assays. The present study showed that biologically synthesized AgNPs using the extract of various parts of D. uncinatum have strong antioxidant and cytotoxic potentials.

Until now, little attention has been paid to the geographic distribution and evaluation of genetic diversity of durum wheat from the Central Fertile Crescent (modern-day Turkey and Syria). Turkey and Syria are considered as primary centers of wheat diversity, and thousands of locally adapted wheat landraces are still present in the farmers' small fields. We planned this study to evaluate the genetic diversity of durum wheat landraces from the Central Fertile Crescent by genotyping based on DArTseq and SNP analysis. A total of 39,568 DArTseq and 20,661 SNP markers were used to characterize the genetic characteristic of 91 durum wheat land races. Clustering based on Neighbor joining analysis, principal coordinate as well as Bayesian model implemented in structure, clearly showed that the grouping pattern is not associated with the geographical distribution of the durum wheat due to the mixing of the Turkish and Syrian landraces. Significant correlation between DArTseq and SNP markers was observed in the Mantel test. However, we detected a non-significant relationship between geographical coordinates and DArTseq (r = -0.085) and SNP (r = -0.039) loci. These results showed that unconscious farmer selection and lack of the commercial varieties might have resulted in the exchange of genetic material and this was apparent in the genetic structure of durum wheat in Turkey and Syria. The genomic characterization presented here is an essential step towards a future exploitation of the available durum wheat genetic resources in genomic and breeding programs. The results of this study have also depicted a clear insight about the genetic diversity of wheat accessions from the Central Fertile Crescent.

This study investigated the forage quality of four naturally occurring Lathyrus species ( Lathyrus czeczottianus , L. pratensis , L. roseus , and L. rotundifolius subsp. miniatus), including one endemic taxon, collected from the Rize province of Turkey. Forage traits of two years were analyzed using the Taguchi design (TD) of experiment to determine trait stability and species-based differences and robustness. Machine learning models (Random forest – RF and Light gradient boosting – LightGBM) were applied to predict forage quality parameters. Results revealed L. pratensis as the most robust species for critical traits such as crude ash ratio, crude protein ratio, and K/(Ca + Mg) based on signal-to-noise ratio. The ML models showed high predictive accuracy for mineral and digestibility-related traits (R² > 0.97). The RF model exhibited superior predictive capacity for fiber traits compared to the LightGBM model. The combined use of TD and ML-based prediction demonstrates a potential framework for evaluating species-level differences, supporting data-driven breeding, optimizing forage quality, and sustainable livestock production in diverse environments. While ML models demonstrated promising predictive performance, future studies incorporating larger datasets and different cross-validation techniques are needed to enhance model robustness and improve the general applicability of these findings. Graphical Abstract