Risk Factors of Crimean-Congo Hemorrhagic Fever in Sindh Province, Pakistan.

OBJECTIVES: In Sindh Province, Pakistan, confirmed Crimean Congo hemorrhagic fever (CCHF) increased from zero in 2008 to 16 in 2015-2016. To counter this increase, in 2016, we initiated structured CCHF surveillance to improve estimates of risk factors for CCHF in Sindh and to identify potential interventions. METHODS: Beginning in 2016, all referral hospitals in Sindh reported all CCHF cases to surveillance agents. We used laboratory-confirmed cases from CCHF surveillance from 2016 to 2020 to compute incidence rates and in a case-control study to quantify risk factors for CCHF. RESULTS: For the 5 years, CCHF incidence was 4.2 per million for the Sindh capital, Karachi, (68 cases) and 0.4 per million elsewhere. Each year, the onset of new cases peaked during the 13 days during and after the 3-day Eid al Adha festival, when Muslims sacrificed livestock, accounting for 38% of cases. In Karachi, livestock for Eid were purchased at a seasonal livestock market that concentrated up to 700,000 livestock. CCHF cases were most common (44%) among the general population that had visited livestock markets (odds ratio = 102). CONCLUSIONS: Urban CCHF in Sindh province is associated with the general public's exposure to livestock markets in addition to high-risk occupations.

IntelliGenes: Interactive and user-friendly multimodal AI/ML application for biomarker discovery and predictive medicine.

Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life; however, its progress in the field of multi-omics is not matching the levels others have attained. Challenges include but are not limited to the handling and analysis of high volumes of complex multi-omics data, and the expertise needed to implement and execute AI/ML approaches. In this article, we present IntelliGenes, an interactive, customizable, cross-platform, and user-friendly AI/ML application for multi-omics data exploration to discover novel biomarkers and predict rare, common, and complex diseases. The implemented methodology is based on a nexus of conventional statistical techniques and cutting-edge ML algorithms, which outperforms single algorithms and result in enhanced accuracy. The interactive and cross-platform graphical user interface of IntelliGenes is divided into three main sections: (i) Data Manager, (ii) AI/ML Analysis, and (iii) Visualization. Data Manager supports the user in loading and customizing the input data and list of existing biomarkers. AI/ML Analysis allows the user to apply default combinations of statistical and ML algorithms, as well as customize and create new AI/ML pipelines. Visualization provides options to interpret a diverse set of produced results, including performance metrics, disease predictions, and various charts. The performance of IntelliGenes has been successfully tested at variable in-house and peer-reviewed studies, and was able to correctly classify individuals as patients and predict disease with high accuracy. It stands apart primarily in its simplicity in use for nontechnical users and its emphasis on generating interpretable visualizations. We have designed and implemented IntelliGenes in a way that a user with or without computational background can apply AI/ML approaches to discover novel biomarkers and predict diseases.

Cloning, expression and purification of cellobiohydrolase gene from Caldicellulosiruptor bescii for efficient saccharification of plant biomass.

Anthropogenic activities have led to a drastic shift from natural fuels to alternative renewable energy reserves that demand heat-stable cellulases. Cellobiohydrolase is an indispensable member of cellulases that play a critical role in the degradation of cellulosic biomass. This article details the process of cloning the cellobiohydrolase gene from the thermophilic bacterium Caldicellulosiruptor bescii and expressing it in Escherichia coli (BL21) CondonPlus DE3-(RIPL) using the pET-21a(+) expression vector. Multi-alignments and structural modeling studies reveal that recombinant CbCBH contained a conserved cellulose binding domain III. The enzyme's catalytic site included Asp-372 and Glu-620, which are either involved in substrate or metal binding. The purified CbCBH, with a molecular weight of 91.8 kDa, displayed peak activity against pNPC (167.93 U/mg) at 65°C and pH 6.0. Moreover, it demonstrated remarkable stability across a broad temperature range (60-80°C) for 8 h. Additionally, the Plackett-Burman experimental model was employed to assess the saccharification of pretreated sugarcane bagasse with CbCBH, aiming to evaluate the cultivation conditions. The optimized parameters, including a pH of 6.0, a temperature of 55°C, a 24-hour incubation period, a substrate concentration of 1.5% (w/v), and enzyme activity of 120 U, resulted in an observed saccharification efficiency of 28.45%. This discovery indicates that the recombinant CbCBH holds promising potential for biofuel sector.

Physicochemical properties and performance of non-woody derived biochars for the sustainable removal of aquatic pollutants: A systematic review.

Biochar is a carbon-rich material produced from the partial combustion of different biomass residues. It can be used as a promising material for adsorbing pollutants from soil and water and promoting environmental sustainability. Extensive research has been conducted on biochars prepared from different feedstocks used for pollutant removal. However, a comprehensive review of biochar derived from non-woody feedstocks (NWF) and its physiochemical attributes, adsorption capacities, and performance in removing heavy metals, antibiotics, and organic pollutants from water systems needs to be included. This review revealed that the biochars derived from NWF and their adsorption efficiency varied greatly according to pyrolysis temperatures. However, biochars (NWF) pyrolyzed at higher temperatures (400-800 °C) manifested excellent physiochemical and structural attributes as well as significant removal effectiveness against antibiotics, heavy metals, and organic compounds from contaminated water. This review further highlighted why biochars prepared from NWF are most valuable/beneficial for water treatment. What preparatory conditions (pyrolysis temperature, residence time, heating rate, and gas flow rate) are necessary to design a desirable biochar containing superior physiochemical and structural properties, and adsorption efficiency for aquatic pollutants? The findings of this review will provide new research directions in the field of water decontamination through the application of NWF-derived adsorbents.

GWAS advancements to investigate disease associations and biological mechanisms.

Genome-wide association studies (GWAS) have been instrumental in elucidating the genetic architecture of various traits and diseases. Despite the success of GWAS, inherent limitations such as identifying rare and ultra-rare variants, the potential for spurious associations, and in pinpointing causative agents can undermine diagnostic capabilities. This review provides an overview of GWAS and highlights recent advances in genetics that employ a range of methodologies, including Whole Genome Sequencing (WGS), Mendelian Randomization (MR), the Pangenome's high-quality T2T-CHM13 panel, and the Human BioMolecular Atlas Program (HuBMAP), as potential enablers of current and future GWAS research. State of the literature demonstrate the capabilities of these techniques in enhancing the statistical power of GWAS. WGS, with its comprehensive approach, captures the entire genome, surpassing the capabilities of the traditional GWAS technique focused on predefined Single Nucleotide Polymorphism (SNP) sites. The Pangenome's T2T-CHM13 panel, with its holistic approach, aids in the analysis of regions with high sequence identity, such as segmental duplications (SDs). Mendelian Randomization has advanced causative inference, improving clinical diagnostics and facilitating definitive conclusions. Furthermore, spatial biology techniques like HuBMAP, enable 3D molecular mapping of tissues at single-cell resolution, offering insights into pathology of complex traits. This study aims to elucidate and advocate for the increased application of these technologies, highlighting their potential to shape the future of GWAS research.

Does perioperative hydrocortisone or indomethacin improve pancreatoduodenectomy outcomes? A triple arm, randomized placebo-controlled trial.

Backgrounds/Aims: This trial evaluated whether anti-inflammatory agents hydrocortisone (H) and indomethacin (I) could reduce major complications after pancreatoduodenectomy (PD). Methods: Between June 2018 and June 2020, 105 patients undergoing PD with > 40% of acini on the intraoperative frozen section were randomized into three groups (35 patients per group): 1) intravenous H 100 mg 8 hourly, 2) rectal I suppository 100 mg 12 hourly, and 3) placebo (P) from postoperative day (POD) 0-2. Participants, investigators, and outcome assessors were blinded. The primary outcome was major complications (Clavien-Dindo grades 3-5). Secondary outcomes were overall complications (Clavien-Dindo grades 1-5), Clinically relevant postoperative pancreatic fistula (CR-POPF), delayed gastric emptying (DGE), postpancreatectomy hemorrhage (PPH), surgical site infections (SSI), length of stay, POD-3 serum amylase, readmission rate, and mortality. Results: Major complications were comparable (8.6%, 5.7%, and 8.6% in groups H, I, and P, respectively). However, overall complications were significantly lower in group H than in group P (45.7% vs. 80.0%, p = 0.006). CR-POPF (14.3% vs. 25.7%, p = 0.371), PPH (8.6% vs. 14.3%, p = 0.710), DGE (8.6% vs. 22.9%, p = 0.188), and SSI (14.3% vs. 25.7%, p = 0.371) were comparable between groups H and P. Major complications and overall complications in group I were 5.7% and 60.0%, respectively, which were comparable to those in groups P and H. CR-POPF rates in groups H, I, and P were 14.3%, 17.1%, and 25.7%, respectively, which was comparable. Conclusions: H and I did not decrease major complications in PD.

Local surface warming assessment in response to vegetation shifts over arid lands of Central Asia (2001-2020).

The Northern Eurasia Earth Science Partnership Initiative (NEESPI) was established to address the large-scale environmental change across this region. Regardless of the increasingly insightful literature addressing vegetation change across Central Asia, the biogeophysical warming effects of vegetation shifts still need to be clarified. To contribute, the utility of robust satellite observation is explored to evaluate the surface warming effects of vegetation shifts across Central Asia, which is among NEEPSI's hotspots. We estimated an average increase of +1.9 °C in daytime local surface temperature and + 1.5 °C in the nighttime due to vegetation shift (2001-2020). Meanwhile, the mean local latent heat increased by 4.65Wm-2, following the mild reduction of emitted longwave radiation (-0.8Wm-2). We found that vegetation shifts led to local surface warming with a bright surface, noting that the average air surface temperature was revealed to have increased significantly (2001-2020). This signal was driven mainly by agricultural expansion in western Kazakhstan stretching to Tajikistan and Xinjiang, then deforestation confined in Tajikistan, southeast Kazakhstan, and the northwestern edge of Xinjiang, and finally, grassland encroachment occurred massively in the west to central Kazakhstan. These findings address the latest information on Central Asia's vegetation shifts that may be substantial in landscape change mitigation plans.

Association of Postoperative Hyperamylasemia With Clinically Relevant Postoperative Pancreatic Fistula in Pancreatoduodenectomy.

Background In this study, we aimed to determine the association between postoperative hyperamylasemia (POH) and clinically relevant postoperative pancreatic fistula (CR-POPF) after pancreatoduodenectomy (PD). Methodology A prospective observational study of 140 consecutive PDs between March 2020 and March 2022 was conducted. POH was defined as an elevation in serum pancreatic amylase levels above the institutional upper limit of normal on postoperative day (POD) 1 (>100 U/L). CR-POPF was defined as the International Study Group of Pancreatic Surgery Grade B or C POPF. The primary outcome was the rate of CR-POPF in the study population. The trial was prospectively registered with Clinicaltrials.gov (NCT04514198). Results In our study, 93 (66.42%) patients had POH (serum amylase >100 U/L). CR-POPF developed in 48 (34.28%) patients: 40 type B and 8 type C. CR-POPF rate was 43.01% (40/93) in patients with POH compared to 17.02% (8/47) in patients without POH (p = 0.0022). Patients with POH had a mean serum amylase of 422.7 ± 358.21 U/L on POD1 compared to 47.2 ± 20.19 U/L in those without POH (p < 0.001). Serum amylase >100 U/L on POD1 was strongly associated with developing CR-POPF (odds ratio = 3.71; 95% confidence interval = 1.31-10.37) on logistic regression, with a sensitivity and specificity of 83.3% and 42.4%, respectively. Blood loss >350 mL, pancreatic duct size <3 mm, and elevated POD1 serum amylase >100 U/L were predictive of CR-POPF on multivariate analysis (p < 0.001). Conclusions An elevated serum amylase on POD1 may help identify patients at risk for developing POPF following PD.

Retraction: Molecular Characterization and Antibiotic Susceptibility Pattern of Bacterial Strains Isolated From Wound of Patients With Diabetes.

[This retracts the article DOI: 10.7759/cureus.47681.].

Non-Ketotic Hyperglycinemia: A Rare Presentation with Neurological and Skeletal Abnormalities.

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Plant root mechanisms and their effects on carbon and nutrient accumulation in desert ecosystems under changes in land use and climate.

Deserts represent key carbon reservoirs, yet as these systems are threatened this has implications for biodiversity and climate change. This review focuses on how these changes affect desert ecosystems, particularly plant root systems and their impact on carbon and mineral nutrient stocks. Desert plants have diverse root architectures shaped by water acquisition strategies, affecting plant biomass and overall carbon and nutrient stocks. Climate change can disrupt desert plant communities, with droughts impacting both shallow and deep-rooted plants as groundwater levels fluctuate. Vegetation management practices, like grazing, significantly influence plant communities, soil composition, root microorganisms, biomass, and nutrient stocks. Shallow-rooted plants are particularly susceptible to climate change and human interference. To safeguard desert ecosystems, understanding root architecture and deep soil layers is crucial. Implementing strategic management practices such as reducing grazing pressure, maintaining moderate harvesting levels, and adopting moderate fertilization can help preserve plant-soil systems. Employing socio-ecological approaches for community restoration enhances carbon and nutrient retention, limits desert expansion, and reduces CO2 emissions. This review underscores the importance of investigating belowground plant processes and their role in shaping desert landscapes, emphasizing the urgent need for a comprehensive understanding of desert ecosystems.

Tuning active sites on biochars for remediation of mercury-contaminated soil: A comprehensive review.

Mercury (Hg) contamination is acknowledged as a global issue and has generated concerns globally due to its toxicity and persistence. Tunable surface-active sites (SASs) are one of the key features of efficient BCs for Hg remediation, and detailed documentation of their interactions with metal ions in soil medium is essential to support the applications of functionalized BC for Hg remediation. Although a specific active site exhibits identical behavior during the adsorption process, a systematic documentation of their syntheses and interactions with various metal ions in soil medium is crucial to promote the applications of functionalized biochars in Hg remediation. Hence, we summarized the BC's impact on Hg mobility in soils and discussed the potential mechanisms and role of various SASs of BC for Hg remediation, including oxygen-, nitrogen-, sulfur-, and X (chlorine, bromine, iodine)- functional groups (FGs), surface area, pores and pH. The review also categorized synthesis routes to introduce oxygen, nitrogen, and sulfur to BC surfaces to enhance their Hg adsorptive properties. Last but not the least, the direct mechanisms (e.g., Hg- BC binding) and indirect mechanisms (i.e., BC has a significant impact on the cycling of sulfur and thus the Hg-soil binding) that can be used to explain the adverse effects of BC on plants and microorganisms, as well as other related consequences and risk reduction strategies were highlighted. The future perspective will focus on functional BC for multiple heavy metal remediation and other potential applications; hence, future work should focus on designing intelligent/artificial BC for multiple purposes.

Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine.

Personalized interventions are deemed vital given the intricate characteristics, advancement, inherent genetic composition, and diversity of cardiovascular diseases (CVDs). The appropriate utilization of artificial intelligence (AI) and machine learning (ML) methodologies can yield novel understandings of CVDs, enabling improved personalized treatments through predictive analysis and deep phenotyping. In this study, we proposed and employed a novel approach combining traditional statistics and a nexus of cutting-edge AI/ML techniques to identify significant biomarkers for our predictive engine by analyzing the complete transcriptome of CVD patients. After robust gene expression data pre-processing, we utilized three statistical tests (Pearson correlation, Chi-square test, and ANOVA) to assess the differences in transcriptomic expression and clinical characteristics between healthy individuals and CVD patients. Next, the recursive feature elimination classifier assigned rankings to transcriptomic features based on their relation to the case-control variable. The top ten percent of commonly observed significant biomarkers were evaluated using four unique ML classifiers (Random Forest, Support Vector Machine, Xtreme Gradient Boosting Decision Trees, and k-Nearest Neighbors). After optimizing hyperparameters, the ensembled models, which were implemented using a soft voting classifier, accurately differentiated between patients and healthy individuals. We have uncovered 18 transcriptomic biomarkers that are highly significant in the CVD population that were used to predict disease with up to 96% accuracy. Additionally, we cross-validated our results with clinical records collected from patients in our cohort. The identified biomarkers served as potential indicators for early detection of CVDs. With its successful implementation, our newly developed predictive engine provides a valuable framework for identifying patients with CVDs based on their biomarker profiles.

Recent trends and economic significance of modified/functionalized biochars for remediation of environmental pollutants.

The pollution of soil and aquatic systems by inorganic and organic chemicals has become a global concern. Economical, eco-friendly, and sustainable solutions are direly required to alleviate the deleterious effects of these chemicals to ensure human well-being and environmental sustainability. In recent decades, biochar has emerged as an efficient material encompassing huge potential to decontaminate a wide range of pollutants from soil and aquatic systems. However, the application of raw biochars for pollutant remediation is confronting a major challenge of not getting the desired decontamination results due to its specific properties. Thus, multiple functionalizing/modification techniques have been introduced to alter the physicochemical and molecular attributes of biochars to increase their efficacy in environmental remediation. This review provides a comprehensive overview of the latest advancements in developing multiple functionalized/modified biochars via biological and other physiochemical techniques. Related mechanisms and further applications of multiple modified biochar in soil and water systems remediation have been discussed and summarized. Furthermore, existing research gaps and challenges are discussed, as well as further study needs are suggested. This work epitomizes the scientific prospects for a complete understanding of employing modified biochar as an efficient candidate for the decontamination of polluted soil and water systems for regenerative development.

Deciphering genomic signatures associating human dental oral craniofacial diseases with cardiovascular diseases using machine learning approaches.

OBJECTIVES: Periodontal diseases are chronic, inflammatory disorders that involve the destruction of supporting tissues surrounding the teeth which leads to permanent damage and substantially heightens systemic exposure. If left untreated, dental, oral, and craniofacial diseases (DOCs), especially periodontitis, can increase an individual's risk in developing complex traits including cardiovascular diseases (CVDs). In this study, we are focused on systematically investigating causality between periodontitis with CVDs with the application of artificial intelligence (AI), machine learning (ML) algorithms, and state-of-the-art bioinformatics approaches using RNA-seq-driven gene expression data of CVD patients. MATERIALS AND METHODS: In this study, we built a cohort of CVD patients, collected their blood samples, and performed RNA-seq and gene expression analysis to generate transcriptomic profiles. We proposed a nexus of AI/ML approaches for the identification of significant biomarkers, and predictive analysis. We implemented recursive feature elimination, Pearson correlation, chi-square, and analysis of variance to detect significant biomarkers, and utilized random forest and support vector machines for predictive analysis. RESULTS: Our AI/ML analyses have led us to the preliminary conclusion that GAS5, GPX1, HLA-B, and SNHG6 are the potential gene markers that can be used to explain the causal relationship between periodontitis and CVDs. CONCLUSIONS: CVDs are relatively common in patients with periodontal disease, and an increased risk of CVD is associated with periodontal disease independent of gender. Genetic susceptibility contributing to periodontitis and CVDs have been suggested to some extent, based on the similar degree of heritability shared between both complex diseases.

Genomic analysis across 53 canine cancer types reveals novel mutations and high clinical actionability potential.

A genomic understanding of the oncogenic processes and individual variability of human cancer has steadily fueled improvement in patient outcomes over the past 20 years. Mutations within tumour tissues are routinely assessed through clinical genomic diagnostic assays by academic and commercial laboratories to facilitate diagnosis, prognosis and effective treatment stratification. The application of genomics has unveiled a wealth of mutation-based biomarkers in canine cancers, suggesting that the transformative principles that have revolutionized human cancer medicine can be brought to bear in veterinary oncology. To advance clinical genomics and genomics-guided medicine in canine oncology, we have developed and validated a canine cancer next-generation sequencing gene panel for the identification of multiple mutation types in clinical specimens. With this panel, we examined the genomic landscapes of 828 tumours from 813 dogs, spanning 53 cancer types. We identified 7856 alterations, encompassing copy number variants, single nucleotide variants, indels and internal tandem duplications. Additionally, we evaluated the clinical utility of these alterations by incorporating a biomarker framework from comprehensive curation of primary canine literature and inferences from human cancer genomic biomarker literature and clinical diagnostics. Remarkably, nearly 90% of the cases exhibited mutations with diagnostic, prognostic or therapeutic implications. Our work represents a thorough assessment of genomic landscapes in a large cohort of canine cancers, the first of its kind for its comprehensive inclusion of multiple mutation types and structured annotation of biomarkers, demonstrating the clinical potential of leveraging mutation-based biomarkers in veterinary oncology.

IntelliGenes: a novel machine learning pipeline for biomarker discovery and predictive analysis using multi-genomic profiles.

SUMMARY: In this article, we present IntelliGenes, a novel machine learning (ML) pipeline for the multi-genomics exploration to discover biomarkers significant in disease prediction with high accuracy. IntelliGenes is based on a novel approach, which consists of nexus of conventional statistical techniques and cutting-edge ML algorithms using multi-genomic, clinical, and demographic data. IntelliGenes introduces a new metric, i.e. Intelligent Gene (I-Gene) score to measure the importance of individual biomarkers for prediction of complex traits. I-Gene scores can be utilized to generate I-Gene profiles of individuals to comprehend the intricacies of ML used in disease prediction. IntelliGenes is user-friendly, portable, and a cross-platform application, compatible with Microsoft Windows, macOS, and UNIX operating systems. IntelliGenes not only holds the potential for personalized early detection of common and rare diseases in individuals, but also opens avenues for broader research using novel ML methodologies, ultimately leading to personalized interventions and novel treatment targets. AVAILABILITY AND IMPLEMENTATION: The source code of IntelliGenes is available on GitHub (https://github.com/drzeeshanahmed/intelligenes) and Code Ocean (https://codeocean.com/capsule/8638596/tree/v1).