Artificial intelligence approaches for early detection of neurocognitive disorders among older adults.

Introduction: Dementia is one of the major global health issues among the aging population, characterized clinically by a progressive decline in higher cognitive functions. This paper aims to apply various artificial intelligence (AI) approaches to detect patients with mild cognitive impairment (MCI) or dementia accurately. Methods: Quantitative research was conducted to address the objective of this study using randomly selected 343 Saudi patients. The Chi-square test was conducted to determine the association of the patient's cognitive function with various features, including demographical and medical history. Two widely used AI algorithms, logistic regression and support vector machine (SVM), were used for detecting cognitive decline. This study also assessed patients' cognitive function based on gender and developed the predicting models for males and females separately. Results: Fifty four percent of patients have normal cognitive function, 34% have MCI, and 12% have dementia. The prediction accuracies for all the developed models are greater than 71%, indicating good prediction capability. However, the developed SVM models performed the best, with an accuracy of 93.3% for all patients, 94.4% for males only, and 95.5% for females only. The top 10 significant predictors based on the developed SVM model are education, bedtime, taking pills for chronic pain, diabetes, stroke, gender, chronic pains, coronary artery diseases, and wake-up time. Conclusion: The results of this study emphasize the higher accuracy and reliability of the proposed methods in cognitive decline prediction that health practitioners can use for the early detection of dementia. This research can also stipulate substantial direction and supportive intuitions for scholars to enhance their understanding of crucial research, emerging trends, and new developments in future cognitive decline studies.

An overview of systematic reviews on predictors of smoking cessation among young people.

Understanding the factors that influence smoking cessation among young people is crucial for planning targeted cessation approaches. The objective of this review was to comprehensively summarize evidence for predictors of different smoking cessation related behaviors among young people from currently available systematic reviews. We searched six databases and reference lists of the included articles for studies published up to October 20, 2023. All systematic reviews summarizing predictors of intention to quit smoking, quit attempts, or smoking abstinence among people aged 10-35 years were included. We excluded reviews on effectiveness of smoking cessation intervention; smoking prevention and other smoking behaviors; cessation of other tobacco products use, dual use, and polysubstance use. We categorized the identified predictors into 5 different categories for 3 overlapping age groups. JBI critical appraisal tool and GRADE-CERqual approach were used for quality and certainty assessment respectively. A total of 11 systematic reviews were included in this study; all summarized predictors of smoking abstinence/quit attempts and two also identified predictors of intention to quit smoking. Seven reviews had satisfactory critical appraisal score and there was minimal overlapping between the reviews. We found 4 'possible' predictors of intention to quit smoking and 119 predictors of smoking abstinence/quit attempts. Most of these 119 predictors were applicable for ~10-29 years age group. We had moderate confidence on the 'probable', 'possible', 'insufficient evidence', and 'inconsistent direction' predictors and low confidence on the 'probably unrelated' factors. The 'probable' predictors include a wide variety of socio-demographic factors, nicotine dependence, mental health, attitudes, behavioral and psychological factors, peer and family related factors, and jurisdictional policies. These predictors can guide improvement of existing smoking cessation interventions or planning of new targeted intervention programs. Other predictors as well as predictors of intention to quit smoking need to be further investigated among adolescents and young adults separately.

Targeting the Cell Cycle, RRM2 and NF-κB for the Treatment of Breast Cancers.

A hallmark of cancer is the dysregulation of the cell cycle. The CDK4/6 inhibitor palbociclib is approved for treating advanced estrogen-receptor-positive breast cancer, but its success is limited by the development of acquired resistance owing to long-term therapy despite promising clinical outcomes. This situation necessitates the development of potential combination strategies. Here, we report that didox, an inhibitor of ribonucleotide reductase in combination with palbociclib, can overcome palbociclib resistance in ER-positive and ER-negative breast cancers. This study shows didox downregulates an element of the cell cycle checkpoint, cyclin D1, accompanied by a reduction in NF-κB activity in vitro and tumor growth inhibition of palbociclib-resistant ER positive breast cancer tumor growth in vivo. Furthermore, didox induces cell cycle arrest at G1 as well as reduces ROS generated by on-target effects of palbociclib on the cell cycle. Our current study also reports that the CCND1 and RRM2 upregulation associated with palbociclib-resistant breast cancers decreases upon ribonucleotide reductase inhibition. Our data present a novel and promising biomarker-driven combination therapeutic approach for the treatment of ER-positive and ER-negative breast cancers that involves the inhibition of the CDK4/6-cyclinD1/pRb cell cycle axis that merits further clinical investigation in human models.

Genetic determinants of SARS-CoV-2 and the clinical outcome of COVID-19 in Southern Bangladesh.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has had a severe impact on population health. The genetic determinants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in southern Bangladesh are not well understood. METHODS: This study aimed to determine the genomic variation in SARS-CoV-2 genomes that have evolved over 2 years of the pandemic in southern Bangladesh and their association with disease outcomes and virulence of this virus. We investigated demographic variables, disease outcomes of COVID-19 patients and genomic features of SARS-CoV-2. RESULTS: We observed that the disease severity was significantly higher in adults (85.3%) than in children (14.7%), because the expression of angiotensin-converting enzyme-2 (ACE-2) diminishes with ageing that causes differences in innate and adaptive immunity. The clade GK (n = 66) was remarkable between June 2021 and January 2022. Because of the mutation burden, another clade, GRA started a newly separated clustering in December 2021. The burden was significantly higher in GRA (1.5-fold) highlighted in mild symptoms of COVID-19 patients than in other clades (GH, GK, and GR). Mutations were accumulated mainly in S (22.15 mutations per segment) and ORF1ab segments. Missense (67.5%) and synonymous (18.31%) mutations were highly noticed in adult patients with mild cases rather than severe cases, especially in ORF1ab segments. Moreover, we observed many unique mutations in S protein in mild cases compared to severe, and homology modeling revealed that those might cause more folding in the protein's alpha helix and beta sheets. CONCLUSION: Our study identifies some risk factors such as age comorbidities (diabetes, hypertension, and renal disease) that are associated with severe COVID-19, providing valuable insight regarding prioritizing vaccination for high-risk individuals and allocating health care and resources. The findings of this work outlined the knowledge and mutational basis of SARS-CoV-2 for the next treatment steps. Further studies are needed to confirm the effects of structural and functional proteins of SARS-CoV-2 in detail for monitoring the emergence of new variants in future.

Examining the disparities of anti-malarial drug consumption among children under the age of five: a study of 5 malaria-endemic countries.

BACKGROUND: Malaria is one of the most prominent illnesses affecting children, ranking as one of the key development concerns for many low- and middle-income countries (LMICs). There is not much information available on the use of anti-malarial drugs in LMICs in children under five. The study aimed to investigate disparities in anti-malarial drug consumption for malaria among children under the age of five in LMICs. METHODS: This study used recent available cross-sectional data from the Malaria Indicator Survey (MIS) datasets across five LMICs (Guinea, Kenya, Mali, Nigeria, and Sierra Leone), which covered a portion of sub-Saharan Africa. The study was carried out between January 2, 2023, and April 15, 2023, and included children under the age of five who had taken an anti-malarial drug for malaria 2 weeks before the survey date. The outcome variable was anti-malarial drug consumption, which was classified into two groups: those who had taken anti-malarial drugs and those who had not. RESULTS: In the study of LMICs, 32,397 children under five were observed, and among them, 44.1% had received anti-malarial drugs. Of the five LMICs, Kenya had the lowest (9.2%) and Mali had the highest (70.5%) percentages of anti-malarial drug consumption. Children under five with malaria are more likely to receive anti-malarial drugs if they are over 1 year old, live in rural areas, have mothers with higher education levels, and come from wealthier families. CONCLUSION: The study emphasizes the importance of developing universal coverage strategies for anti-malarial drug consumption at both the national and local levels. The study also recommends that improving availability and access to anti-malarial drugs may be necessary, as the consumption of these drugs for treating malaria in children under the age of five is shockingly low in some LMICs.

Exploring therapeutic potential of Woodfordia fruticosa (L.) Kurz leaf and bark focusing on antioxidant, antithrombotic, antimicrobial, anti-inflammatory, analgesic, and antidiarrheal properties.

Background and Aims: The study aimed to evaluate the pharmacological properties of methanolic extracts of leaves and barks of Woodfordia fruticosa (L.) Kurz (family: Lythraceae) focusing on antioxidant, thrombolytic, anti-inflammatory, antibacterial, analgesic, and antidiarrheal effects. Methods: 1,1-Diphenyl-2-picrylhydrazyl (DPPH) free radical scavenging assay, clot lysis, disc diffusion, and membrane stabilizing methods were employed to assess in vitro antioxidant, thrombolytic, antibacterial, and anti-inflammatory properties of the leaf and bark methanolic extracts (ME) of W. fruticosa and different organic solvents, that is, petroleum ether (PE), dichloromethane (DCM), chloroform (CL), and aqueous (AQ) fractions. In addition, in vivo central and peripheral analgesic and antidiarrheal activities of both crude extracts were evaluated at two doses (200 and 400 mg/kg of body weight [bw]). Results: All the extracts and fractions showed promising antioxidant properties by scavenging DDPH free radicals with IC50 of 6.11-20.79 µg/mL. AQ fraction (41.24%) of leaves and ME (44.90%) of bark exerted notable in vitro thrombolytic activity. The CL fraction of leaves and AQ fraction of the bark showed 43.16% and 45.37% inhibition of RBC hemolysis, respectively, compared to the inhibition of RBC hemolysis by aspirin in a hypotonic-induced membrane stabilizing assay. Besides, both extracts were observed to provide significant (p < 0.001) central and peripheral analgesic responses at both doses of 200 and 400 mg/kg bw. Furthermore, both doses of bark extract (p < 0.001) and the 400 mg/kg bw of leaf extract (p < 0.05) were observed to possess statistically significant antidiarrheal activity. Additionally, in an in vivo acute toxicity investigation, both extracts had a median lethal dose (LD50) greater than 5000 mg/kg bw, indicating their safety level. Conclusion: The current study proves the ethnomedicinal uses of W. fruticosa; however, further studies are required for phytochemical screening to isolate the responsible bioactive compounds and discover the lead molecules from the plant species.

Association of Metabolites, Nutrients, and Toxins in Maternal and Cord Serum with Asthma, IgE, SPT, FeNO, and Lung Function in Offspring.

The role of metabolites, nutrients, and toxins (MNTs) in sera at the end of pregnancy and of their association with offspring respiratory and allergic disorders is underexplored. Untargeted approaches detecting a variety of compounds, known and unknown, are limited. In this cohort study, we first aimed at discovering associations of MNTs in grandmaternal (F0) serum with asthma, immunoglobulin E, skin prick tests, exhaled nitric oxide, and lung function parameters in their parental (F1) offspring. Second, for replication, we tested the identified associations of MNTs with disorders in their grandchildren (F2-offspring) based on F2 cord serum. The statistical analyses were sex-stratified. Using liquid chromatography/high-resolution mass spectrometry in F0, we detected signals for 2286 negative-ion lipids, 59 positive-ion lipids, and 6331 polar MNTs. Nine MNTs (one unknown MNT) discovered in F0-F1 and replicated in F2 showed higher risks of respiratory/allergic outcomes. Twelve MNTs (four unknowns) constituted a potential protection in F1 and F2. We recognized MNTs not yet considered candidates for respiratory/allergic outcomes: a phthalate plasticizer, an antihistamine, a bile acid metabolite, tryptophan metabolites, a hemiterpenoid glycoside, triacylglycerols, hypoxanthine, and polyphenol syringic acid. The findings suggest that MNTs are aspirants for clinical trials to prevent adverse respiratory/allergic outcomes.

Occurrence of shigellosis in pediatric diarrheal patients in Chattogram, Bangladesh: A molecular based approach.

Shigellaa Gram-negative, non-motile bacillus, is the primary causative agent of the infectious disease shigellosis, which kills 1.1 million people worldwideevery year. The children under the age of five are primarily the victims of this disease. This study has been conducted to assess the prevalence of shigellosis through selective plating, biochemical test and conventional PCR assays, where the samples were collected from suspected diarrheoal patients. Invasive plasmid antigen H (ipaH) and O-antigenic rfc gene were used to identify Shigella spp. and S. flexneri respectively. For validation of these identification, PCR product of ipaH gene of a sample (Shigella flexneri MZS 191) has been sequenced and submitted to NCBI database (GenBank accession no- MW774908.1). Further this strain has been used as positive control. Out of 204, around 14.2% (n = 29)(P> 0.01) pediatric diarrheoal cases were screened as shigellosis. Another interesting finding was that most of shigellosis affected children were 7 months to 1 year (P> 0.01).The significance of this study lies in the analyses of the occurrenceand the molecular identification of Shigellaspp. and S. flexneri that can be utilized in improving the accurate identification and the treatment of the most severe and alarming shigellosis.

Food insecurity and health outcome nexus: empirical evidence from the informal sector enterprises in Bangladesh.

BACKGROUND: Food insecurity indicates the difficulty of constantly obtaining adequate food because of limited economic resources. Food insecurity challenges the desired health outcomes. Although extensive literature has examined the associations between food security and health, low-wage informal sector workers have been less frequently addressed in this topic. The present study has focused on food insecurity among the workers working in the informal sector enterprises who experienced entrenched disadvantage during COVID-19 and examines the relationship between food insecurity and health status as measured by self-reported physical and mental health conditions. METHODS: This study has utilized cross-sectional data collected from workers working in informal manufacturing and business enterprises in Dhaka city of Bangladesh. The Food Insecurity Experience Scale (FIES) with eight items is used to screen for food insecurity, and the Short Form 12v2 (SF12v2) scale with 12 questions, and validated for use with Bengali respondents, is used to measure the health status of the informal workers. A health production function has been constructed where the health status (both physical and mental) of workers is associated with food insecurity and other socio-economic and health care factors. Empirical analyses of the study have included descriptive statistics, mean score comparisons, and multivariate regression analyses to identify the predictive factors of the physical and mental health status of the workers. RESULTS: A moderate to severe food insecurity is found to be responsible for the poor health status (both physical and mental) of the selected working group population. Moreover, age over 40 years, having a large family, dissatisfaction with the work place, and the prevalence of occupational health risks are linked to lower physical health, while dissatisfaction with the work place and the incidence of severe diseases contribute to poor mental health status along with food insecurity. CONCLUSIONS: Extending social and economic protection towards health coverage and basic consumption is suggested as an immediate action to save lives and ensure productivity of the informal workers. Besides, an increase in income and ensuring decent working conditions are also recommended for the health, safety and satisfaction of workers working in informal sector enterprises.

Investigating the prospect of cleaner production in informal enterprises: A scientific assessment of environmental burdens and economic efficiency.

The present study aims to assess the prospects for cleaner production (CP) and sustainable development (SD) of informally operated small manufacturing enterprises, which are frequently blamed for uncontrolled waste disposal and causing pollution to the environment. The economic efficiency level of these firms has been explored to this end, and the metallic pollution loads in the surrounding environment have been scientifically analyzed to investigate the nexus between these two. DEA (Data Envelopment Analysis)-Tobit analysis has been employed, and a pollution load index (PLI) of heavy metal pollution comprising two environmental compartments (soil and water) has been constructed based on the concentration level of metalloid pollutants in the samples collected from the surrounding areas of the studied informal firms in Bangladesh. The study disproves CP practice in majority of the informal firms in Bangladesh by observing a positive relationship between firm-level efficiency and pollution load sourced from their production activities. Afterwards, this study estimates the eco-efficiency level of firms by considering pollution load as an undesirable output and minimizing its impact in an input-oriented DEA model. Applying the eco-efficiency scores in censored Tobit regression analysis, the outcome endorses the prospect of CP for informally operated enterprises in Bangladesh. However, the CP prospect can only materialize if and only if firms are provided with adequate technical, financial, and strategic support for achieving eco-efficiency in their production. The informal and marginal nature of the studied firms restricts them from getting access to the facilities and support services needed for implementing CP and moving towards sustainable manufacturing. Therefore, this study recommends green practices in informal manufacturing and limiting the informal firms by bringing them gradually under the coverage of formalization, which is in line with the achievement of the targets mentioned in Sustainable Development Goal 8.

Microplastics in freshwater wild and farmed fish species of Bangladesh.

In today's world, microplastic contamination of aquatic systems is a subject of major concern. In this study, the incidence of microplastics in freshwater fish from both farmed and wild sources was evaluated. Wild catla (Gibelion catla), stinging catfish (Heteropneustes fossilis), rui (Labeo rohita), tengara catfish (Mystus tengara), and pabdah catfish (Ompok pabda) were collected from Padma River, Turag River, and Bhairab River. Farmed fishes of same species were collected from Mymensingh and Chandpur fish markets. A total of 68 (43.6%) microparticles out of 156 were identified as microplastics using ATR-FTIR. About 66.67% (20 out of 30) of farmed fish samples had microplastics in their digestive system, while 88.4% (23 of 26) of wild fish samples presented microplastics. There was a significant difference (p < 0.05) in average microplastic count between wild and farmed fish. Average microplastic count also showed a significant difference among five fish species (p < 0.01). A variety of polymers were observed, with polyethylene being the most abundant, followed by polyester, polypropylene, polyamide-6, and polyurethane. This study suggests that microplastic contamination is ubiquitous and contamination is common in all of the fish species investigated, independent of their origin (wild or farmed). However, wild fish population is at a greater risk of ingesting microplastics. This study, for the first time ever, reports plastic pollution in freshwater fishes of Bangladesh from wild and farmed sources.

Anticancer potential of phytochemicals from Oroxylum indicum targeting Lactate Dehydrogenase A through bioinformatic approach.

In recent years, small molecule inhibition of LDHA (Lactate Dehydrogenase A) has evolved as an appealing option for anticancer therapy. LDHA catalyzes the interconversion of pyruvate and lactate in the glycolysis pathway to play a crucial role in aerobic glycolysis. Therefore, in the current investigation LDHA was targeted with bioactive phytochemicals of an ethnomedicinally important plant species Oroxylum indicum (L.) Kurz. A total of 52 phytochemicals were screened against LDHA protein through molecular docking, ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) assay and molecular dynamics simulation to reveal three potential lead compounds such as Chrysin-7-O-glucuronide (-8.2 kcal/mol), Oroxindin (-8.1 kcal/mol) and Oroxin A (-8.0 kcal/mol). ADMET assay unveiled favorable pharmacokinetic, pharmacodynamic and toxicity properties for all the lead compounds. Molecular dynamics simulation exhibited significant conformational stability and compactness. MM/GBSA free binding energy calculations further corroborated the selection of top candidates where Oroxindin (-46.47 kcal/mol) was found to be better than Chrysin-7-O-glucuronide (-45.72 kcal/mol) and Oroxin A (-37.25 kcal/mol). Aldolase reductase and Xanthine dehydrogenase enzymes were found as potential drug targets and Esculin, the FDA approved drug was identified as structurally analogous to Oroxindin. These results could drive in establishing novel medications targeting LDHA to fight cancer.

Investigating Antimicrobial Resistance and ESBL Producing Gene in Klebsiella Isolates among Neonates and Adolescents in Southern Bangladesh.

Background: Multidrug-resistant (MDR) clones of Klebsiella pneumoniae (Kpn) have been increasingly documented in community-acquired and nosocomial infections all around the globe. Extended-spectrum ß-lactamases (ESBLs) are a rapidly evolving group of ß-lactamase enzymes derived from SHV genes by mutations. This research work aimed to investigate and analyze the widespread prevalence of Kpn antibiotic resistance in different areas of the southern part of Bangladesh. Methods: This particular study was executed and implemented by using 501 clinical samples or isolates from two different hospitals in Chattogram. The disk diffusion method was used to detect Kpn's sensitivity to 16 antibiotics in a drug susceptibility test. By using the PCR technique, the widespread prevalence of antibiotic-resistant gene blaSHV-11 was studied. Sequencing along with phylogenetic analysis was utilized to verify isolates with the blaSHV-11 gene. Results: Almost all of the Kpn isolates were spotted to be antibiotic-resistant. These Kpn isolates were resistant to ß-lactams, aminoglycosides, and quinolones at high levels. The spatial analysis displayed that infections involving Kpn were more common in the urban areas (70%) than in the rural areas (30%). Neonates had substantially higher levels (p < 0.001) of resistance to multidrug than other age groups. Cefepime was identified as the most frequent antibiotic-resistant to all age groups (56.68%). The highest numbers of resistant isolates (36.92%) were found in urine samples. The ESBL gene blaSHV-11 was found in 38% isolates. Conclusion: The significant frequency of MDR Kpn harboring ß-lactamases and AMR genes strongly suggests the requirement to develop effective antimicrobial resistance control and prevention measures in Bangladesh.

Environmental quality and its nexus with informal economy, corruption control, energy use, and socioeconomic aspects: the perspective of emerging economies.

This paper explores the impacts of informal economic activities and institutional capacity, particularly, corruption control on the environmental quality degradation of emerging economies under the prevailing socio-economic conditions and energy use patterns of the countries. The study utilizes key environmental degradation indicators: Carbon dioxide (CO2) emissions, ecological footprints (EFs), and Nitrous Oxide (NO) emissions, and a panel dataset of 15 emerging countries for the period 2002-2019 to undertake an empirical investigation. The pooled mean group (PMG)-ARDL estimator, Fully Modified OLS (FMOLS), Dynamic OLS (DOLS) and Augmented Mean Group (AMG) methods have been applied as empirical investigation techniques. The empirical findings reveal that in the long-run informal economic activities positively affect the environmental quality with fewer recorded emissions of CO2 and EFs while these activities affect negatively to NO emissions. This study has also found that corruption control improves environmental quality by reducing EFs and NO emissions but works to the opposite by increasing recorded CO2 emissions. An increase in economic growth and renewable energy consumption improves environmental quality in emerging countries, while consumption of non-renewable energy degrades the environmental quality. The robust empirical findings advocate policy initiatives for intense monitoring of informal activities and implementation of indirect tax policy to regulate informal activities and the pollution they cause. Careful measures of corruption control and initiatives to bring the informal economic activities into a formal framework are suggested to reduce CO2 and NO emissions. An increase in economic growth with more focus on renewables and phasing out non-renewables can ensure green growth in emerging countries.

Environmental kuznets curve and causal links between environmental degradation and selected socioeconomic indicators in Bangladesh.

This study has investigated the Environmental Kuznets Curve (EKC) hypothesis and focuses on the resource stock of the country in relation to the selection of environmental degradation indicators. Acknowledging the critical role of the EKC for policy formulation and development strategies, this study has examined the validity of the EKC hypothesis by exploring the relationship between economic growth, urbanization, energy consumption, trade openness, human capital and ecological footprints for the period 1972-2018 in Bangladesh. The autoregressive distributive lag bounds testing approach is applied for this purpose, taking into account the structural break in the time series. A fully modified OLS estimation has also been applied as the robustness check of the results. Results from the empirical analysis confirm the existence of the EKC in Bangladesh in both the long run and short run. The causal nexus among the variables is examined by applying the Vector Error Correction Granger causality test. The causality test result shows that economic growth and urbanization cause ecological footprints in both the short run and the long run. Based on this result, it can be inferred that economic growth activities in Bangladesh can be continued and extended with minimal ecological cost through structural economic change and proper environmental management.

Predicting sun protection measures against skin diseases using machine learning approaches.

BACKGROUND: The substantial growth rate of skin cancer has necessitated adequate protection from solar radiation. Consequently, analyzing sun protection practices is an imperative research area in dermatology and pharmacology. AIMS: This paper aims to analyze public sun-protection manners in the Arabian Peninsula regions. METHODS: A simple random survey was conducted to assess public sun protection manners. Artificial neural network (ANN) and support vector machine (SVM) were selected from several machine learning algorithms to create the models for predicting public sun protection measures based on the prediction accuracy. Model performances were evaluated based on several performance indicators depending on the confusion matrices and receiver operating characteristic curves. RESULTS: 51% of the respondents have a low level, and 49% have a high level of sun protection practices. The results showed that the SVM performed considerably amended than the ANN for predicting the response. The relative importance of the predictors for the best predictive SVM model was also analyzed. The predictors are ranked as: the number of times having sunburnt >gender > use seat belt while driving/riding a vehicle >considers the UV index for personal sun exposure >income based on the expenses >sports/exercise activities >consciousness of the chance for having sunburnt on extended exposure to the sun >age > having any skin problem >nationality > skin type. CONCLUSION: These identified significant predictors might be considered for developing an effective policy to increase public consciousness using proper protection from solar radiation's detrimental effect to rule out skin diseases.

Hybrid support vector regression and crow search algorithm for modeling and multiobjective optimization of microalgae-based wastewater treatment.

Microalgae-based wastewater treatment (and biomass production) is an environmentally benign and energetically efficient technique as compared to traditional practices. The present study is focused on optimization of the major treatment variables such as temperature, light-dark cycle (LD), and nitrogen (N)-to-phosphate (P) ratio (N/P) for the elimination of N and P from tertiary municipal wastewater utilizing Chlorella kessleri microalgae species. In this regard, a hybrid support vector regression (SVR) technique integrated with the crow search algorithm has been applied as a novel modeling/optimization tool. The SVR models were formulated using the experimental data, which were furnished according to the response surface methodology with Box-Behnken Design. Various statistical indicators, including mean absolute percentage error, Taylor diagram, and fractional bias, confirmed the superior performance of SVR models as compared to the response surface methodology (RSM) and generalized linear model (GLM). Finally, the best SVR model was hybridized with the crow search algorithm for single/multi-objective optimizations to acquire the global optimal treatment conditions for maximum N and P removal efficiencies. The best-operating conditions were found to be 29.3°C, 24/0 h/h of LD, and 6:1 of N/P, with N and P elimination efficiencies of 99.97 and 93.48%, respectively. The optimized values were further confirmed by new experimental data.

Genome Sequences of SARS-CoV-2 Sublineage B.1.617.2 Strains from 12 Children in Chattogram, Bangladesh.

We announce the complete genome sequences of 12 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sublineage B.1.617.2 strains (Delta variant) obtained from nasopharyngeal and oropharyngeal swab samples from 12 pediatric patients in Chittagong, Bangladesh, displaying COVID-19 symptoms. Oxford Nanopore MinION sequencing technology was used to generate the genomic sequences.

Prevalence and molecular characterization of antibiotic resistance and associated genes in Klebsiella pneumoniae isolates: A clinical observational study in different hospitals in Chattogram, Bangladesh.

OBJECTIVE: This study was performed to investigate the prevalence of multidrug resistance and molecular characterization of Klebsiella pneumoniae (KPN) from clinical isolates in the southern region of Bangladesh. Additional analysis of the prevalence of blaNDM-1, blaSHV-11, uge genes of KPN was also carried out among these clinical isolates. METHOD: The study was carried out using 1000 clinical isolates collected from two different hospitals of Chattogram. A drug susceptibility test was performed by the disk diffusion method to detect KPN's response to 16 antibiotics. The presence of antibiotic-resistant and (or) virulent genes blaNDM-1, blaSHV-11, uge were investigated using the PCR technique. Isolates having blaNDM-1, blaSHV-11, uge gene were further validated by sequencing followed by phylogenetic analysis. Phylogenetic relationships among these isolates were determined by Clustal omega and MEGA7. RESULT: A total of 79%, 77%, 74.9%, 71%, 66% and 65% isolates exhibited resistance against cefuroxime, cefixime, cefotaxime, ceftazidime, cefepime and ceftriaxone respectively. The frequency of resistance to other antibiotics varied from 26.5% to 61.8%. PCR analysis showed that 64% of strains harbored blaNDM-1 gene, and 38% strains harbored blaSHV-11 gene. Moreover, 47% of samples were carrying uge gene, and 19% of samples carried blaNDM-1, blaSHV-11, uge genes together. CONCLUSION: In this study, we've analysed the pattern of expression as well as prevalence of blaNDM-1, blaSHV-11, and uge genes in Klebsiella isolates. Upon molecular and statistical analysis, we found a high prevalence of multi-drug resistance KPN strains in the isolates. The Klebsiella isolates were confirmed to harbor multiple ESBL genes and 64% of the isolates were found to be producing NDM-1. As multidrug resistance is an alarming issue, continuous surveillance and routine clinical detection of resistant bacteria and plasmids are necessary to prevent catastrophic public health incidents.

Molecular Targeting of RRM2, NF-κB, and Mutant TP53 for the Treatment of Triple-Negative Breast Cancer.

Doxorubicin and other anthracycline derivatives are frequently used as part of the adjuvant chemotherapy regimen for triple-negative breast cancer (TNBC). Although effective, doxorubicin is known for its off-target and toxic side effect profile, particularly with respect to the myocardium, often resulting in left ventricular (LV) dysfunction and congestive heart failure when used at cumulative doses exceeding 400 mg/m2 Previously, we have observed that the ribonucleotide reductase subunit M2 (RRM2) is significantly overexpressed in estrogen receptor (ER)-negative cells as compared with ER-positive breast cancer cells. Here, we inhibited RRM2 in ER-negative breast cancer cells as a target for therapy in this difficult-to-treat population. We observed that through the use of didox, a ribonucleotide reductase inhibitor, the reduction in RRM2 was accompanied by reduced NF-κB activity in vitro When didox was used in combination with doxorubicin, we observed significant downregulation of NF-κB proteins accompanied by reduced TNBC cell proliferation. As well, we observed that protein levels of mutant p53 were significantly reduced by didox or combination therapy in vitro Xenograft studies showed that combination therapy was found to be synergistic in vivo, resulting in a significantly reduced tumor volume as compared with doxorubicin monotherapy. In addition, the use of didox was also found to ameliorate the toxic myocardial effects of doxorubicin in vivo as measured by heart mass, LV diameter, and serum troponin T levels. The data present a novel and promising approach for the treatment of TNBC that merits further clinical evaluation in humans.