Mask exhibits trxG-like behavior and associates with H3K27ac marked chromatin.

The Trithorax group (trxG) proteins counteract the repressive effect of Polycomb group (PcG) complexes and maintain transcriptional memory of active states of key developmental genes. Although chromatin structure and modifications appear to play a fundamental role in this process, it is not clear how trxG prevents PcG-silencing and heritably maintains an active gene expression state. Here, we report a hitherto unknown role of Drosophila Multiple ankyrin repeats single KH domain (Mask), which emerged as one of the candidate trxG genes in our reverse genetic screen. The genome-wide binding profile of Mask correlates with known trxG binding sites across the Drosophila genome. In particular, the association of Mask at chromatin overlaps with CBP and H3K27ac, which are known hallmarks of actively transcribed genes by trxG. Importantly, Mask predominantly associates with actively transcribed genes in Drosophila. Depletion of Mask not only results in the downregulation of trxG targets but also correlates with diminished levels of H3K27ac. The fact that Mask positively regulates H3K27ac levels in flies was also found to be conserved in human cells. Strong suppression of Pc mutant phenotype by mutation in mask provides physiological relevance that Mask contributes to the anti-silencing effect of trxG, maintaining expression of key developmental genes. Since Mask is a downstream effector of multiple cell signaling pathways, we propose that Mask may connect cell signaling with chromatin mediated epigenetic cell memory governed by trxG.

Dynamic Imaging of Blood Coagulation Within the Hematoma of Patients With Acute Hemorrhagic Stroke.

BACKGROUND: The dynamics of blood clot (combination of Hb [hemoglobin], fibrin, and a higher concentration of aggregated red blood cells) formation within the hematoma of an intracerebral hemorrhage is not well understood. A quantitative neuroimaging method of localized coagulated blood volume/distribution within the hematoma might improve clinical decision-making. METHODS: The deoxyhemoglobin of aggregated red blood cells within extravasated blood exhibits a higher magnetic susceptibility due to unpaired heme iron electrons. We propose that coagulated blood, with higher aggregated red blood cell content, will exhibit (1) a higher positive susceptibility than noncoagulated blood and (2) increase in fibrin polymerization-restricted localized diffusion, which can be measured noninvasively using quantitative susceptibility mapping and diffusion tensor imaging. In this serial magnetic resonance imaging study, we enrolled 24 patients with acute intracerebral hemorrhage between October 2021 to May 2022 at a stroke center. Patients were 30 to 70 years of age and had a hematoma volume >15 cm3 and National Institutes of Health Stroke Scale score >1. The patients underwent imaging 3×: within 12 to 24 (T1), 36 to 48 (T2), and 60 to 72 (T3) hours of last seen well on a 3T magnetic resonance imaging system. Three-dimensional anatomic, multigradient echo and 2-dimensional diffusion tensor images were obtained. Hematoma and edema volumes were calculated, and the distribution of coagulation was measured by dynamic changes in the susceptibilities and fractional anisotropy within the hematoma. RESULTS: Using a coagulated blood phantom, we demonstrated a linear relationship between the percentage coagulation and susceptibility (R2=0.91) with a positive red blood cell stain of the clot. The quantitative susceptibility maps showed a significant increase in hematoma susceptibility (T1, 0.29±0.04 parts per millions; T2, 0.36±0.04 parts per millions; T3, 0.45±0.04 parts per millions; P<0.0001). A concomitant increase in fractional anisotropy was also observed with time (T1, 0.40±0.02; T2, 0.45±0.02; T3, 0.47±0.02; P<0.05). CONCLUSIONS: This quantitative neuroimaging study of coagulation within the hematoma has the potential to improve patient management, such as safe resumption of anticoagulants, the need for reversal agents, the administration of alteplase to resolve the clot, and the need for surgery.

Development of depression in patients using androgen deprivation therapy: A systemic review and meta-analysis.

BACKGROUND: Androgen deprivation therapy (ADT) is an effective treatment for advanced prostate cancer (PCa). Multiple studies have highlighted serious consequences this therapy poses to mental health, particularly depression. We aimed to review the incidence and association between ADT in men with PCa and the risk of depression. METHODS: We systematically searched multiple databases, including MEDLINE, Scopus till August 2023 for studies that compared ADT versus control for treating PCa reporting depression as outcome. Meta-analysis was performed using random-effects models and results presented as odds ratios (ORs) with 95% confidence interval (CI). Quality assessment of the included studies was conducted using Joanna Briggs Institute critical appraisal checklists. RESULTS: A total of 38 studies (17 retrospective studies, 16 prospective studies, two cross-sectional studies and two randomized trials) with 360,650 subjects met the inclusion criteria and were included in this meta-analysis. The estimated pooled incidence of depression among ADT patients is 209.5 (95% CI = 122.3; 312.2) per 1000 patients. There is statistically significant relationship between ADT treatment and depression (OR = 1.46, 95% CI = 1.28, 1.67; p = 0, I2 = 86.4%). The results remained consistent across various subgroups. No risk of publication bias was detected by funnel plot and Eggers's test (p > 0.05). CONCLUSION: There is a higher risk of depression for men receiving ADT. Further studies evaluating optimal treatments for depression in men on ADT are warranted.

Enhancing nitrogen use efficiency and yield of maize (Zea mays L.) through Ammonia volatilization mitigation and nitrogen management approaches.

Management of nitrogen (N) fertilizer is a critical factor that can improve maize (Zea mays L.) production. On the other hand, high volatilization losses of N also pollute the air. A field experiment was established using a silt clay soil to examine the effect of sulfur-coated urea and sulfur from gypsum on ammonia (NH3) emission, N use efficiency (NUE), and the productivity of maize crop under alkaline calcareous soil. The experimental design was a randomized complete block (RCBD) with seven treatments in three replicates: control with no N, urea150 alone (150 kg N ha-1), urea200 alone (200 kg N ha-1), urea150 + S (60 kg ha-1 S from gypsum), urea200 + S, SCU150 (sulfur-coated urea) and SCU200. The results showed that the urea150 + S and urea200 + S significantly reduced the total NH3 by (58 and 42%) as compared with the sole application urea200. The NH3 emission reduced further in the treatment with SCU150 and SCU200 by 74 and 65%, respectively, compared to the treatment with urea200. The maize plant biomass, grain yield, and total N uptake enhanced by 5-14%, 4-17%, and 7-13, respectively, in the treatments with urea150 + s and urea200 + S, relative to the treatment with urea200 alone. Biomass, grain yield, and total N uptake further increased significantly by 22-30%, 25-28%, and 26-31%, respectively, in the treatments with SCU150 and SCU200, relative to the treatment with urea200 alone. The applications of SCU150 enhanced the nitrogen use efficiency (NUE) by (72%) and SCU200 by (62%) respectively, compared with the sole application of urea200 alone. In conclusion, applying S-coated urea at a lower rate of 150 kg N ha-1 compared with a higher rate of 200 kg N ha-1 may be an effective way to reduce N fertilizer application rate and mitigate NH3 emission, improve NUE, and increase maize yield. More investigations are suggested under different soil textures and climatic conditions to declare S-coated urea at 150 kg N ha-1 as the best application rate for maize to enhance maize growth and yield.

Exome sequencing in four families with neurodevelopmental disorders: genotype-phenotype correlation and identification of novel disease-causing variants in VPS13B and RELN.

Neurodevelopmental disorders (NDDs) are a clinically and genetically heterogeneous group of early-onset pediatric disorders that affect the structure and/or function of the central or peripheral nervous system. Achieving a precise molecular diagnosis for NDDs may be challenging due to the diverse genetic underpinnings and clinical variability. In the current study, we investigated the underlying genetic cause(s) of NDDs in four unrelated Pakistani families. Using exome sequencing (ES) as a diagnostic approach, we identified disease-causing variants in established NDD-associated genes in all families, including one hitherto unreported variant in RELN and three recurrent variants in VPS13B, DEGS1, and SPG11. Overall, our study highlights the potential of ES as a tool for clinical diagnosis.

Doped Porous Carbon Spheres with Controllable Vesicle Structure: Preparation and the Effects of Pore Size on Electromagnetic Wave Absorption Properties.

This work reports on the preparation of uniform vesicle-structural carbon spheres doped with heteroatoms of N, P, and S, with the pore sizes strictly controlled by the hard templates of monodisperse submicron SiO2 spheres. The uniformly doped vesicular carbon microspheres are obtained in three steps: Stöber hydrolysis for the SiO2; in situ polymerization for the immobilization; and alkaline etching after carbonization. The size of the vesicles can be easily adjusted by regulating the particle size of the submicron SiO2 spheres, which has a significant effect on its electromagnetic wave (EMW) absorption performance. Compared with microspheres with pore sizes of 180 and 480 nm, when the vesicle aperture is 327 nm, with only 5.5 wt.% filling load and 1.9 mm thickness, the material shows the best EMW absorption behavior with the effective absorption bandwidth (EAB) covers the entire Ku band (6.32 GHz) and the minimum reflection loss (RLmin) of -36.10 dB, suggesting the optimized pore size of the microspheres can significantly improve the overall impedance matching of the material and achieve broadband wave absorption. This work paves the way for the enhancement of EMW absorption properties of porous material by optimizing the pore size of uniform apertures while maintaining their composition.

Comparison between the outcomes of transfemoral access and transfemoral access with adjunct upper extremity access in patients undergoing endovascular aortic repair: A pilot systematic review and meta-analysis.

Endovascular aortic repair is an emerging novel intervention for the management of abdominal aortic aneurysms. It is crucial to compare the effectiveness of different access sites, such as transfemoral access (TFA) and upper extremity access (UEA). An electronic literature search was conducted using PubMed, EMBASE, and Google Scholar databases. The primary endpoint was the incidence of stroke/transient ischemic attack (TIA), while the secondary endpoints included technical success, access-site complications, mortality, myocardial infarction (MI), spinal cord ischemia, among others. Forest plots were constructed for the pooled analysis of data using the random-effects model in Review Manager, version 5.4. Statistical significance was set at p < 0.05. Our findings in 9403 study participants (6228 in the TFA group and 3175 in the UEA group) indicate that TFA is associated with a lower risk of stroke/TIA [RR: 0.55; 95% CI: 0.40-0.75; p = 0.0002], MI [RR: 0.51; 95% CI: 0.38-0.69; p < 0.0001], spinal cord ischemia [RR: 0.41; 95% CI: 0.32-0.53, p < 0.00001], and shortens fluoroscopy time [SMD: -0.62; 95% CI: -1.00 to -0.24; p = 0.001]. Moreover, TFA required less contrast agent [SMD: -0.33; 95% CI: -0.61 to -0.06; p = 0.02], contributing to its appeal. However, no significant differences emerged in technical success [p = 0.23], 30-day mortality [p = 0.48], ICU stay duration [p = 0.09], or overall hospital stay length [p = 0.22]. Patients with TFA had a lower risk of stroke, MI, and spinal cord ischemia, shorter fluoroscopy time, and lower use of contrast agents. Future large-scale randomized controlled trials are warranted to confirm and strengthen these findings.

Microplastic contamination in the agricultural soil-mitigation strategies, heavy metals contamination, and impact on human health: a review.

Microplastic pollution has emerged as a critical global environmental issue due to its widespread distribution, persistence, and potential adverse effects on ecosystems and human health. Although research on microplastic pollution in aquatic environments has gained significant attention. However, a limited literature has summarized the impacts of microplastic pollution the agricultural land and human health. Therefore, In the current review, we have discussed how microplastic(s) affect the microorganisms by ingesting the microplastic present in the soil, alternatively affecting the belowground biotic and abiotic components, which further elucidates the negative effects on the above-ground properties of the crops. In addition, the consumption of these crops in the food chain revealed a potential risk to human health throughout the food chain. Moreover, microplastic pollution has the potential to induce a negative impact on agricultural production and food security by altering the physiochemical properties of the soil, microbial population, nutrient cycling, and plant growth and development. Therefore, we discussed in detail the potential hazards caused by microplastic contamination in the soil and through the consumption of food and water by humans in daily intake. Furthermore, further study is urgently required to comprehend how microplastic pollution negatively affects terrestrial ecosystems, particularly agroecosystems which drastically reduces the productivity of the crops. Our review highlights the urgent need for greater awareness, policy interventions, and technological solutions to address the emerging threat of microplastic pollution in soil and plant systems and mitigation strategies to overcome its potential impacts on human health. Based on existing studies, we have pointed out the research gaps and proposed different directions for future research.

A Journey of Innovation: 40 years of Pioneering Medical Education at the Aga Khan University Medical College in Karachi, Pakistan.

This article presents an overview of Aga Khan University's (AKU) pioneering medical education initiatives over the past 40 years, exploring its impact on healthcare in the region and its commitment to advancing medical education and research in the developing world. Established in 1983 as the first private university in Pakistan, AKU has evolved into a global institution with a focus on improving healthcare standards and addressing healthcare needs in the developing world. The article also discusses the undergraduate and postgraduate medical education programs at AKU Medical College, Pakistan, highlighting their unique features and pioneering approaches to medical education. The institution's journey highlights its ability to adapt to the evolving healthcare landscape while maintaining a focus on quality and excellence, offering a model for other institutions striving to meet healthcare needs in low- and middle-income countries.

Patient-resident physician communication - a qualitative study to assess the current state, challenges and possible solutions.

BACKGROUND: Patients perceive effective patient-doctor communication as an important metric when evaluating their satisfaction with health systems. Hence, optimal patient-physician communication is fundamental for quality healthcare. High-income countries (HICs) have extensively studied patient-resident communication. However, there is a dearth of similar studies in low- and middle-income countries (LMICs). Therefore, we aimed to explore the current state of and barriers to practicing good patient-resident communication and explore possible solutions to mitigate these challenges at one of the largest Academic Medical Centers in an LMIC. METHODS: This study employed an exploratory qualitative study design and was conducted at the Aga Khan University Hospital in Pakistan. Through purposive maximum variation sampling, 60 healthcare workers from diverse cohorts, including attendings, fellows, residents, and medical students, participated in eight focus group discussions. RESULTS: We identified three key themes from the data: Status-quo of residents' communication skills and learning (Poor verbal and non-verbal communication, inadequate training programs, and variable sources of learning), Barriers to effective communication (Institutional barriers such as lack of designated counselling spaces, lack of resident insight regarding effective communication and deficits in intra-team communication), and the need for developing a communication skills curriculum (Design, implementation and scaling to other cohorts of healthcare workers). CONCLUSIONS: Findings from this study show that multifaceted factors are responsible for inadequate patient resident-physician communication, highlighting the need for and importance of developing a formal communication skills training curriculum for residents. These insights can be used to create standardized training for equipping residents with adequate skills for effectively communicating with patients which can improve healthcare service delivery and patient outcomes.

DFT, Molecular Docking, ADME, and Cardiotoxicity studies of Persuasive Thiazoles as Potential Inhibitors of the Main Protease of SARS-CoV-2.

This study explores the capability of thiazoles as potent inhibitors of SARS-CoV-2 Mpro. Seventeen thiazoles (1-17) were screened for their linking affinity with the active site of SARS-CoV-2 Mpro and compared with the FDA-recommended antiviral drugs, Remdesivir and Baricitinib. Density Functional Theory (DFT) calculations provided electronic and energetic properties of these ligands, shedding light on their stability and reactivity. Molecular docking analysis revealed that thiazole derivatives exhibited favorable linking affinities with various functional sites of SARS-CoV-2 proteins, including spike receptor-linking zone, nucleocapsid protein N-terminal RNA linking zone, and Mpro. Notably, compounds 3, 10, and 12 displayed the best interaction with 6LZG as compared to FDA-approved antiviral drugs Remdesivir and Baricitinib, while compounds 1, 10, and 8 exhibited strong linking with 6M3M and also better than Remdesivir and Baricitinib. Additionally, compounds 3, 1, and 6 showed promising interactions with 6LU7 but only compound 3 performed better than Baricitinib. An ADME (Absorption, Distribution, Metabolism, and Excretion) study provided insights into the pharmacokinetics and drug-likeness of these compounds, with all ligands demonstrating good physicochemical characteristics, lipophilicity, water solubility, pharmacokinetics, drug-likeness, and medicinal chemistry attributes. The results suggest that these selected thiazole derivatives hold promise as potential candidates for further drug development.

Diagnostic utility of anti-thyroid peroxidase immunohistochemistry in the identification of papillary thyroid carcinoma.

The routine histomorphological assessment of follicular thyroid neoplasms has been subject to interobserver or intraobserver variability among histopathologists. Anti-thyroid peroxidase (anti-TPO) has emerged as a useful immunohistochemical (IHC) marker, with its expression lost in papillary thyroid carcinoma (PTC). Our study aims to determine the diagnostic accuracy of anti-TPO IHC expression in the identifying PTC and its variants, particularly the Follicular variant of papillary thyroid carcinoma (FVPTC), with H&E assessment as the gold standard. Anti-TPO IHC (DAKO-MoAb47) was performed on 110 cases, including 76 malignant tumors (classic PTC, FVPTC, follicular carcinoma (FC), and oncocytic carcinoma (OC)) and 34 benign tumors (non-invasive follicular tumor with papillary-like nuclear features (NIFTP) and follicular adenoma (FA)). The loss of expression in more than or equal to 51 % of thyrocytes was considered suggestive of a PTC profile. The sensitivity of the loss of anti-TPO expression for identifying PTC among all carcinomas was 61.7 %, specificity was 75 %, positive predictive value was 90.2 %, negative predictive value was 34.2 %, and accuracy was 64.4 %. The loss of anti-TPO IHC expression combined with routine H&E assessment, supports the identification of PTC and its variants.

Biochar and saline soil: mitigation strategy by incapacitating the ecological threats to agricultural land.

Soil salinity caused a widespread detrimental issue that hinders productivity in agriculture and ecological sustainability, while waste-derived soil amendments like biochar have drawn attention for their capacity to act as a mitigating agent, by enhancing the physical and chemical features of soil, and contributing to the recovery of agricultural waste resources. However, the information concerning biochar and salinity which affect the physicochemical characteristics of soils, crop physiology, and growth is limited. To investigate whether biochar mitigates the salinity stress on wheat crop seedlings, we grow them with salinity stress (120 mM), and biochar (20 tons ha-1), and its interactive effects. The soil properties of soil organic carbon (SOC), soil organic matter (SOM), dissolved organic carbon (DOC), and soil available phosphorus (SAP) decreased in the saline soil by 36.71%, 46.97%, 26.31%, and 15.00%, while biochar treatment increased SOC, DOC, and SAP contents by 7.42%, 31.57%, and 15.00%, respectively. On the other hand, dissolved organic nitrogen (DON) contents decreased in all the treatments compared to the control. The root growth traits, SPAD values, leaf nitrogen, photosynthetic parameters, antioxidant enzymes, and reactive oxygen species decreased in the saline treatment while increasing in the biochar and interactive treatment. Thus, these activities resulted in higher leaves and root biomass in the biochar treatment alone and interactive treatment of salinity and biochar. According to principal component analysis, redundancy analysis, and the mantel test, using biochar in conjunction with salinity treatment was found to be more effective than salinity treatment alone. The results of this study suggest that biochar can be used as a sustainable agricultural technique and a means of mitigation agent by lowering soil salinity while increasing the biomass of crops.

Biochar improves the physical and nutritional quality of soil and plant function.Salinity stress declined the physiological activities and biomass of the crop.Biochar mitigates the salinity stress in soil and enhances the plant functioning.Exposure to both treatments enhances the antioxidant enzyme activity and biomass.

Current Novel Targeted Therapeutic Strategies in Multiple Myeloma.

Multiple myeloma (MM) is a hematologic malignancy caused by the clonal expansion of immunoglobulin-producing plasma cells in the bone marrow and/or extramedullary sites. Common manifestations of MM include anemia, renal dysfunction, infection, bone pain, hypercalcemia, and fatigue. Despite numerous recent advancements in the MM treatment paradigm, current therapies demonstrate limited long-term effectiveness and eventual disease relapse remains exceedingly common. Myeloma cells often develop drug resistance through clonal evolution and alterations of cellular signaling pathways. Therefore, continued research of new targets in MM is crucial to circumvent cumulative drug resistance, overcome treatment-limiting toxicities, and improve outcomes in this incurable disease. This article provides a comprehensive overview of the landscape of novel treatments and emerging therapies for MM grouped by molecular target. Molecular targets outlined include BCMA, GPRC5D, FcRH5, CD38, SLAMF7, BCL-2, kinesin spindle protein, protein disulfide isomerase 1, peptidylprolyl isomerase A, Sec61 translocon, and cyclin-dependent kinase 6. Immunomodulatory drugs, NK cell therapy, and proteolysis-targeting chimera are described as well.

Assessing the Potential of Aconitum Laeve Extract for Biogenic Silver and Gold Nanoparticle Synthesis and Their Biological and Catalytic Applications.

The adoption of green chemistry protocols in nanoparticle (NP) synthesis has exhibited substantial potential and is presently a central focus in research for generating versatile NPs applicable across a broad spectrum of applications. In this scientific contribution, we, for the first time, examined the ability of Aconitum Laeve (A. Laeve) crude extract to synthesize silver and gold nanoparticles (AgNPs@AL; AuNP@AL) and explored their potential applications in biological activities and the catalytic degradation of environmental pollutants. The synthesized NPs exhibited a distinctive surface plasmon resonance pattern, a spherical morphology with approximate sizes of 5-10 nm (TEM imaging), a crystalline architecture (XRD analysis), and potential functional groups identified by FTIR spectroscopy. The antibacterial activity was demonstrated by inhibition zones that measured 16 and 14 mm for the AgNPs@AL and AuNP@AL at a concentration of 80 µg/mL against Staphylococcus aureus and 14 and 12 mm against Escherichia coli, respectively. The antioxidant potential of the synthesized NPs was evaluated using 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2-Phenyl-4,4,5,5-tetramethylimidazoline-1-oxyl 3-Oxide (PTIO), and 3-ethylbenzothiazoline-6-sulfonic acid (ABTS) assays. Our findings suggest that the AuNP@AL effectively countered the tested radicals considerably, displaying IC50 values of 115.9, 103.54, and 180.85 µg/mL against DPPH, PTIO, and ABTS, respectively. In contrast, the AgNPs@AL showed IC50 values of 144.9, 116.36, and 95.39 µg/mL against the respective radicals. In addition, both the NPs presented significant effectiveness in the photocatalytic degradation of methylene blue and rhodamine B. The overall observations indicate that A. Laeve possesses a robust capability to synthesize spherical nanoparticles, exhibiting excellent dispersion and showcasing potential applications in both biological activities and environmental remediation.

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting.

The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron's, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. After a thorough evaluation, the model architectures most suitable for the specific conditions in the UAE and Malaysia were identified. Our study contributes significantly to the ongoing efforts to combat the COVID-19 pandemic, providing crucial insights into the application of sophisticated deep learning algorithms for the precise and timely forecasting of COVID-19 cases. These insights hold substantial value for shaping public health strategies, enabling authorities to develop targeted and evidence-based interventions to manage the virus spread and its impact on the populations of the UAE and Malaysia. The study confirms the usefulness of deep learning methodologies in efficiently processing complex datasets and generating reliable projections, a skill of great importance in healthcare and professional settings.

AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges to the public health sector, including that of the United Arab Emirates (UAE). The objective of this study was to assess the efficiency and accuracy of various deep-learning models in forecasting COVID-19 cases within the UAE, thereby aiding the nation's public health authorities in informed decision-making. METHODS: This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators. Several advanced deep learning models, including long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, and recurrent neural network (RNN) models, were trained and evaluated. Bayesian optimization was also implemented to fine-tune these models. RESULTS: The evaluation framework revealed that each model exhibited different levels of predictive accuracy and precision. Specifically, the RNN model outperformed the other architectures even without optimization. Comprehensive predictive and perspective analytics were conducted to scrutinize the COVID-19 dataset. CONCLUSION: This study transcends academic boundaries by offering critical insights that enable public health authorities in the UAE to deploy targeted data-driven interventions. The RNN model, which was identified as the most reliable and accurate for this specific context, can significantly influence public health decisions. Moreover, the broader implications of this research validate the capability of deep learning techniques in handling complex datasets, thus offering the transformative potential for predictive accuracy in the public health and healthcare sectors.

Making MS Omics Data ML-Ready: SpeCollate Protocols.

The increasing complexity and volume of mass spectrometry (MS) data have presented new challenges and opportunities for proteomics data analysis and interpretation. In this chapter, we provide a comprehensive guide to transforming MS data for machine learning (ML) training, inference, and applications. The chapter is organized into three parts. The first part describes the data analysis needed for MS-based experiments and a general introduction to our deep learning model SpeCollate-which we will use throughout the chapter for illustration. The second part of the chapter explores the transformation of MS data for inference, providing a step-by-step guide for users to deduce peptides from their MS data. This section aims to bridge the gap between data acquisition and practical applications by detailing the necessary steps for data preparation and interpretation. In the final part, we present a demonstrative example of SpeCollate, a deep learning-based peptide database search engine that overcomes the problems of simplistic simulation of theoretical spectra and heuristic scoring functions for peptide-spectrum matches by generating joint embeddings for spectra and peptides. SpeCollate is a user-friendly tool with an intuitive command-line interface to perform the search, showcasing the effectiveness of the techniques and methodologies discussed in the earlier sections and highlighting the potential of machine learning in the context of mass spectrometry data analysis. By offering a comprehensive overview of data transformation, inference, and ML model applications for mass spectrometry, this chapter aims to empower researchers and practitioners in leveraging the power of machine learning to unlock novel insights and drive innovation in the field of mass spectrometry-based omics.