Wildland firefighter work history and clinical indicators of cardiovascular health.

OBJECTIVE: To determine the association between the occupational history as a wildland firefighter (WFF) and clinical indicators of cardiovascular health. METHODS: Among 2,862 WFFs we evaluated associations between the number of total days assigned on fire and high-risk categories of three clinically measured cardiovascular indicators. RESULTS: Almost one-third (32%) of WFFs had one or more clinical measures that would place them in high-risk categories for BMI, blood pressure, and total cholesterol. WFF work history was associated with some of these measures: odds ratio (and 95% confidence interval) for highest versus lowest tertile of days on fire were 1.4 (1.2, 1.8) and 1.2 (1.0, 1.5) for high-risk categories of BMI and cholesterol, respectively. CONCLUSION: More frequent screening and targeted health promotion programs for WFFs are warranted to increase awareness of cardiovascular risk and prevention strategies.

A systematic first-principles investigation of the structural, electronic, mechanical, optical, and thermodynamic properties of Half-Heusler ANiX (ASc, Ti, Y, Zr, Hf; XBi, Sn) for spintronics and optoelectronics applications.

This paper is the first to look at the structural, electronic, mechanical, optical, and thermodynamic properties of the ANiX (ASc, Ti, Y, Zr, Hf; XBi, Sn) half-Heusler (HH) using DFT based first principles method. The lattice parameters that we have calculated are very similar to those obtained in prior investigations with theoretical and experimental data. The positive phonon dispersion curve confirm the dynamical stability of ANiX (ASc, Ti, Y, Zr, Hf; XBi, Sn). The electronic band structure and DOS confirmed that the studied materials ANiX (ASc, Ti, Y, Zr, Hf; XBi, Sn) are direct band gap semiconductors. The investigation also determined significant constants, including dielectric function, absorption, conductivity, reflectivity, refractive index, and loss function. These optical observations unveiled our compounds potential utilization in various electronic and optoelectronic device applications. The elastic constants were used to fulfill the Born criteria, confirming the mechanical stability and ductility of the solids ANiX (ASc, Ti, Y, Zr, Hf; XBi, Sn). The calculated elastic modulus revealed that our studied compounds are elastically anisotropic. Moreover, ANiX (ASc, Ti, Y, Zr, Hf; XBi, Sn) has a very low minimum thermal conductivity (Kmin), and a low Debye temperature (θD), which indicating their appropriateness for utilization in thermal barrier coating (TBC) applications. The Helmholtz free energy (F), internal energy (E), entropy (S), and specific heat capacity (Cv) are determined by calculations derived from the phonon density of states.

AMP-RNNpro: a two-stage approach for identification of antimicrobials using probabilistic features.

Antimicrobials are molecules that prevent the formation of microorganisms such as bacteria, viruses, fungi, and parasites. The necessity to detect antimicrobial peptides (AMPs) using machine learning and deep learning arises from the need for efficiency to accelerate the discovery of AMPs, and contribute to developing effective antimicrobial therapies, especially in the face of increasing antibiotic resistance. This study introduced AMP-RNNpro based on Recurrent Neural Network (RNN), an innovative model for detecting AMPs, which was designed with eight feature encoding methods that are selected according to four criteria: amino acid compositional, grouped amino acid compositional, autocorrelation, and pseudo-amino acid compositional to represent the protein sequences for efficient identification of AMPs. In our framework, two-stage predictions have been conducted. Initially, this study analyzed 33 models on these feature extractions. Then, we selected the best six models from these models using rigorous performance metrics. In the second stage, probabilistic features have been generated from the selected six models in each feature encoding and they are aggregated to be fed into our final meta-model called AMP-RNNpro. This study also introduced 20 features with SHAP, which are crucial in the drug development fields, where we discover AAC, ASDC, and CKSAAGP features are highly impactful for detection and drug discovery. Our proposed framework, AMP-RNNpro excels in the identification of novel Amps with 97.15% accuracy, 96.48% sensitivity, and 97.87% specificity. We built a user-friendly website for demonstrating the accurate prediction of AMPs based on the proposed approach which can be accessed at http://13.126.159.30/ .

DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features.

Lung diseases are the third-leading cause of mortality in the world. Due to compromised lung function, respiratory difficulties, and physiological complications, lung disease brought on by toxic substances, pollution, infections, or smoking results in millions of deaths every year. Chest X-ray images pose a challenge for classification due to their visual similarity, leading to confusion among radiologists. To imitate those issues, we created an automated system with a large data hub that contains 17 datasets of chest X-ray images for a total of 71,096, and we aim to classify ten different disease classes. For combining various resources, our large datasets contain noise and annotations, class imbalances, data redundancy, etc. We conducted several image pre-processing techniques to eliminate noise and artifacts from images, such as resizing, de-annotation, CLAHE, and filtering. The elastic deformation augmentation technique also generates a balanced dataset. Then, we developed DeepChestGNN, a novel medical image classification model utilizing a deep convolutional neural network (DCNN) to extract 100 significant deep features indicative of various lung diseases. This model, incorporating Batch Normalization, MaxPooling, and Dropout layers, achieved a remarkable 99.74% accuracy in extensive trials. By combining graph neural networks (GNNs) with feedforward layers, the architecture is very flexible when it comes to working with graph data for accurate lung disease classification. This study highlights the significant impact of combining advanced research with clinical application potential in diagnosing lung diseases, providing an optimal framework for precise and efficient disease identification and classification.

Effects of Commercial Probiotics on the Growth Performance, Intestinal Microbiota and Intestinal Histomorphology of Nile Tilapia (Oreochromis niloticus) Reared in Biofloc Technology (BFT).

Though different types of commercial probiotics are supplemented in biofloc technology (BFT), very little information is available on their effects on the farmed fish. Therefore, this study focused on evaluating the effects of three most commonly used commercial probiotics on the growth performance, intestinal histomorphology, and intestinal microbiota of Nile tilapia (Oreochromis niloticus) reared in BFT. Tilapia fry, with an average weight of 3.02 ± 0.50 g, were stocked at a density of 60 fry/0.2 m3, and cultured for 90 days. Three commercial probiotics were administered, with three replications for each: a single-genus multi-species probiotic (Bacillus spp.) (T1), a multi-genus multi-species probiotic (Bacillus sp., Lactobacillus sp., Nitrosomonas sp., Nitrobacter sp.) (T2), and a multi-species probiotic (Bacillus spp.) combined with enzymes including amylase, protease, cellulase, and xylanase (T3). The results showed significant variations in growth and feed utilization, with T3 outperforming other treatments in terms of weight gain, liver weight, and intestine weight. Adding Bacillus spp. with enzymes (T3) to water significantly increased the histomorphological parameters (villi length, villi depth, crypt depth, muscle thickness, intestinal thickness) as well as microbes (total viable count and total lactic acid bacteria) of intestine of fish compared to T1 and T2, leading to improved digestion and absorption responses. It is concluded that the supplementation of commercial probiotics has potential benefits on farmed fish species in BFT.

Patient Satisfaction With the Health Care Services of a Government-Financed Health Protection Scheme in Bangladesh: Cross-Sectional Study.

BACKGROUND: Since 2016, the government of Bangladesh has been piloting a health protection scheme known as Shasthyo Surokhsha Karmasuchi (SSK), which specifically targets households living below the poverty line. This noncontributory scheme provides enrolled households access to inpatient health care services for 78 disease groups. Understanding patients' experiences with health care utilization from the pilot SSK scheme is important for enhancing the quality of health care service delivery during the national-level scale-up of the scheme. OBJECTIVE: We aimed to evaluate patient satisfaction with the health care services provided under the pilot health protection scheme in Bangladesh. METHODS: A cross-sectional survey was conducted with the users of the SSK scheme from August to November 2019. Patients who had spent a minimum of 2 nights at health care facilities were selected for face-to-face exit interviews. During these interviews, we collected information on patients' socioeconomic characteristics, care-seeking experiences, and level of satisfaction with various aspects of health care service delivery. To measure satisfaction, we employed a 5-point Likert scale (very satisfied, 5; satisfied, 4; neither satisfied nor dissatisfied, 3; dissatisfied, 2; very dissatisfied, 1). Descriptive statistics, statistical inferential tests (t-test and 1-way ANOVA), and linear regression analyses were performed. RESULTS: We found that 55.1% (241/438) of users were either very satisfied or satisfied with the health care services of the SSK scheme. The most satisfactory indicators were related to privacy maintained during diagnostic tests (mean 3.91, SD 0.64), physicians' behaviors (mean 3.86, SD 0.77), services provided at the registration booth (mean 3.86, SD 0.62), confidentiality maintained regarding diseases (mean 3.78, SD 0.72), and nurses' behaviors (mean 3.60, SD 0.83). Poor satisfaction was identified in the interaction of patients with providers about illness-related information (mean 2.14, SD 1.40), availability of drinking water (mean 1.46, SD 0.76), cleanliness of toilets (mean 2.85, SD 1.04), and cleanliness of the waiting room (mean 2.92, SD 1.09). Patient satisfaction significantly decreased by 0.20 points for registration times of 16-30 minutes and by 0.32 points for registration times of >30 minutes compared with registration times of ≤15 minutes. Similarly, patient satisfaction significantly decreased with an increase in the waiting time to obtain services. However, the satisfaction of users significantly increased if they received a complete course of medicines and all prescribed diagnostic services. CONCLUSIONS: More than half of the users were satisfied with the services provided under the SSK scheme. However, there is scope for improving user satisfaction. To improve the satisfaction level, the SSK scheme implementation authorities should pay attention to reducing the registration time and waiting time to obtain services and improving the availability of drugs and prescribed diagnostic services. The authorities should also ensure the supply of drinking water and enhance the cleanliness of the facility.

A novel investigation of pressure-induced semiconducting to metallic transition of lead free novel Ba3SbI3 perovskite with exceptional optoelectronic properties.

The structural, electronic, mechanical, and optical characteristics of barium-based halide perovskite Ba3SbI3 under the influence of pressures ranging from 0 to 10 GPa have been analyzed using first-principles calculations for the first time. The new perovskite Ba3SbI3 material was shown to be a direct band gap semiconductor at 0 GPa, but the band gap diminished when the applied pressure increased from 0 to 10 GPa. So the Ba3SbI3 material undergoes a transition from semiconductor to metallic due to high pressure at 10 GPa. The Ba3SbI3 material also exhibits an increase in optical absorption and conductivity with applied pressure due to the change in band gap, which is more suitable for solar absorbers, surgical instruments, and optoelectronic devices. The charge density maps confirm the presence of both ionic and covalent bonding characteristics. Exploration into the mechanical characteristics indicates that the Ba3SbI3 perovskite is mechanically stable. Additionally, the Ba3SbI3 compound becomes strongly anisotropic at high pressure. The insightful results of our simulations will all be helpful for the experimental structure of a new effective Ba3SbI3-based inorganic perovskite solar cell in the near future.

ANNprob-ACPs: A novel anticancer peptide identifier based on probabilistic feature fusion approach.

Anticancer Peptides (ACPs) offer significant potential as cancer treatment drugs in this modern era. Quickly identifying active compounds from protein sequences is crucial for healthcare and cancer treatment. In this paper ANNprob-ACPs, a novel and effective model for detecting ACPs has been implemented based on nine feature encoding techniques, including AAC, CC, W2V, DPC, PAAC, QSO, CTDC, CTDT, and CKSAAGP. After analyzing the performance of several machine learning models, the six best models were selected based on their overall performances in every evaluation metric. The probability scores of each model were subsequently aggregated and used as input of our meta- model, called ANNprob-ACPs. Our model outperformed all others and its potential to lead to phenomenal identification of ACPs. The results of this study showed notable improvement in 10-fold cross-validation and independent test, with accuracy of 93.72% and 90.62%, respectively. Our proposed model, ANNprob-ACPs outperformed existing approaches in terms of accuracy and effectiveness in discovering ACPs. By using SHAP, this study obtained the physicochemical properties of QSO, and compositional properties of DPC, AAC, and PAAC are more impactful for our model's performances, which have a major impact on a drug's interactions and future discoveries. Consequently, this model is crucial for the future and has a high probability of detecting ACPs more frequently. We developed a web server of ANNprob-ACPs, which is accessible at ANNprob-ACPs webserver.

Subclinical Measures of Cardiovascular Health Among Wildland Firefighters.

OBJECTIVE: The aim of the study is to compare subclinical measures of cardiovascular health among wildland firefighters (WFFs) to the US general population. METHODS: Our cross-sectional study compared body mass index, total cholesterol, and blood pressure in 11,051 WFFs aged 17 to 64 years using Department of the Interior Medical Screening Program clinical screening examinations between 2014-2018 to National Health and Nutrition Examination Survey of 2015-2016 cycle using adjusted logistic regression analyses. RESULTS: The logistic regression model shows significantly higher odds of hypertension and prehypertension in WFFs (2.84 times more with 95% CI: 2.28-3.53) than US general population. There were no consistent differences in body mass index or total cholesterol between the two population. CONCLUSIONS: Hypertension and prehypertension were more prevalent in WFFs compared with the US general population, which suggests the need for actions for protecting against cardiovascular disease among WFFs.

The effectiveness of a government-sponsored health protection scheme in reducing financial risks for the below-poverty-line population in Bangladesh.

The Government of Bangladesh is piloting a non-contributory health protection scheme called Shasthyo Surokhsha Karmasuchi (SSK) to increase access to quality essential healthcare services for the below-poverty-line (BPL) population. This paper assesses the effect of the SSK scheme on out-of-pocket expenditure (OOPE) for healthcare, catastrophic health expenditure (CHE) and economic impoverishment of the enrolled population. A comparative cross-sectional study was conducted in Tangail District, where the SSK was implemented. From August 2019 to March 2020, a total of 2315 BPL households (HHs) (1170 intervention and 1145 comparison) that had at least one individual with inpatient care experience in the last 12 months were surveyed. A household is said to have incurred CHE if their OOPE for healthcare exceeds the total (or non-food) HH's expenditure threshold. Multiple regression analysis was performed using OOPE, incidence of CHE and impoverishment as dependent variables and SSK membership status, actual BPL status and benefits use status as the main explanatory variables. Overall, the OOPE was significantly lower (P < 0.01) in the intervention areas (Bangladeshi Taka (BDT) 23 366) compared with the comparison areas (BDT 24 757). Regression analysis revealed that the OOPE, CHE incidence at threshold of 10% of total expenditure and 40% of non-food expenditure and impoverishment were 33% (P < 0.01), 46% (P < 0.01), 42% (P < 0.01) and 30% (P < 0.01) lower, respectively, in the intervention areas than in the comparison areas. Additionally, HHs that utilized SSK benefits experienced even lower OOPE by 92% (P < 0.01), CHE incidence at 10% and 40% threshold levels by 72% (P < 0.01) and 59% (P < 0.01), respectively, and impoverishment by 27% at 10% level of significance. These findings demonstrated the significant positive effect of the SSK in reducing financial burdens associated with healthcare utilization among the enrolled HHs. This illustrates the importance of the nationwide scaling up of the scheme in Bangladesh to reduce the undue financial risk of healthcare utilization for those in poverty.

Idiotopes of antibodies against HIV-1 CD4-induced epitope shared with those against microorganisms.

Induction of antibodies (Abs) against the conformational CD4-induced (CD4i) epitope is frequent in HIV-1 infection. However, the mechanism of development of anti-CD4i Abs is unclear. We used anti-idiotypic (aID) monoclonal Abs (mAbs) of anti-CD4i mAbs to isolate anti-CD4i mAbs from infected subjects and track the causative antigens. One anti-aID mAb sorted from infected subjects by aID mAbs had the characteristics of anti-CD4i Abs, including IGHV1-69 usage and ability to bind to HIV-1 Env enhanced by sCD4. Critical amino acid sequences for the binding of six anti-aID mAbs, with shared idiotope to anti-CD4i mAbs, were analysed by phage display. The identified amino acid sequences showed similarity to proteins from human microbiota and infectious agents. Peptides synthesized from Caudoviricetes sp and Vibrio vulnificus based on the identified sequences were reactive to most anti-aID and some anti-CD4i mAbs. These results suggest that anti-CD4i Abs may evolve from B cells primed by microorganisms.

Development of an automated optimal distance feature-based decision system for diagnosing knee osteoarthritis using segmented X-ray images.

Knee Osteoarthritis (KOA) is a leading cause of disability and physical inactivity. It is a degenerative joint disease that affects the cartilage, cushions the bones, and protects them from rubbing against each other during motion. If not treated early, it may lead to knee replacement. In this regard, early diagnosis of KOA is necessary for better treatment. Nevertheless, manual KOA detection is time-consuming and error-prone for large data hubs. In contrast, an automated detection system aids the specialist in diagnosing KOA grades accurately and quickly. So, the main objective of this study is to create an automated decision system that can analyze KOA and classify the severity grades, utilizing the extracted features from segmented X-ray images. In this study, two different datasets were collected from the Mendeley and Kaggle database and combined to generate a large data hub containing five classes: Grade 0 (Healthy), Grade 1 (Doubtful), Grade 2 (Minimal), Grade 3 (Moderate), and Grade 4 (Severe). Several image processing techniques were employed to segment the region of interest (ROI). These included Gradient-weighted Class Activation Mapping (Grad-Cam) to detect the ROI, cropping the ROI portion, applying histogram equalization (HE) to improve contrast, brightness, and image quality, and noise reduction (using Otsu thresholding, inverting the image, and morphological closing). Besides, the focus filtering method was utilized to eliminate unwanted images. Then, six feature sets (morphological, GLCM, statistical, texture, LBP, and proposed features) were generated from segmented ROIs. After evaluating the statistical significance of the features and selection methods, the optimal feature set (prominent six distance features) was selected, and five machine learning (ML) models were employed. Additionally, a decision-making strategy based on the six optimal features is proposed. The XGB model outperformed other models with a 99.46 % accuracy, using six distance features, and the proposed decision-making strategy was validated by testing 30 images.

Graph neural network-based breast cancer diagnosis using ultrasound images with optimized graph construction integrating the medically significant features.

PURPOSE: An automated computerized approach can aid radiologists in the early diagnosis of breast cancer. In this study, a novel method is proposed for classifying breast tumors into benign and malignant, based on the ultrasound images through a Graph Neural Network (GNN) model utilizing clinically significant features. METHOD: Ten informative features are extracted from the region of interest (ROI), based on the radiologists' diagnosis markers. The significance of the features is evaluated using density plot and T test statistical analysis method. A feature table is generated where each row represents individual image, considered as node, and the edges between the nodes are denoted by calculating the Spearman correlation coefficient. A graph dataset is generated and fed into the GNN model. The model is configured through ablation study and Bayesian optimization. The optimized model is then evaluated with different correlation thresholds for getting the highest performance with a shallow graph. The performance consistency is validated with k-fold cross validation. The impact of utilizing ROIs and handcrafted features for breast tumor classification is evaluated by comparing the model's performance with Histogram of Oriented Gradients (HOG) descriptor features from the entire ultrasound image. Lastly, a clustering-based analysis is performed to generate a new filtered graph, considering weak and strong relationships of the nodes, based on the similarities. RESULTS: The results indicate that with a threshold value of 0.95, the GNN model achieves the highest test accuracy of 99.48%, precision and recall of 100%, and F1 score of 99.28%, reducing the number of edges by 85.5%. The GNN model's performance is 86.91%, considering no threshold value for the graph generated from HOG descriptor features. Different threshold values for the Spearman's correlation score are experimented with and the performance is compared. No significant differences are observed between the previous graph and the filtered graph. CONCLUSION: The proposed approach might aid the radiologists in effective diagnosing and learning tumor pattern of breast cancer.

Deep learning-based analysis of COVID-19 X-ray images: Incorporating clinical significance and assessing misinterpretation.

COVID-19, pneumonia, and tuberculosis have had a significant effect on recent global health. Since 2019, COVID-19 has been a major factor underlying the increase in respiratory-related terminal illness. Early-stage interpretation and identification of these diseases from X-ray images is essential to aid medical specialists in diagnosis. In this study, (COV-X-net19) a convolutional neural network model is developed and customized with a soft attention mechanism to classify lung diseases into four classes: normal, COVID-19, pneumonia, and tuberculosis using chest X-ray images. Image preprocessing is carried out by adjusting optimal parameters to preprocess the images before undertaking training of the classification models. Moreover, the proposed model is optimized by experimenting with different architectural structures and hyperparameters to further boost performance. The performance of the proposed model is compared with eight state-of-the-art transfer learning models for a comparative evaluation. Results suggest that the COV-X-net19 outperforms other models with a testing accuracy of 95.19%, precision of 96.49% and F1-score of 95.13%. Another novel approach of this study is to find out the probable reason behind image misclassification by analyzing the handcrafted imaging features with statistical evaluation. A statistical analysis known as analysis of variance test is performed, to identify at which point the model can identify a class accurately, and at which point the model cannot identify the class. The potential features responsible for the misclassification are also found. Moreover, Random Forest Feature importance technique and Minimum Redundancy Maximum Relevance technique are also explored. The methods and findings of this study can benefit in the clinical perspective in early detection and enable a better understanding of the cause of misclassification.

Portable optical spectroscopic assay for non-destructive measurement of key metabolic parameters on in vitro cancer cells and organotypic fresh tumor slices.

To enable non-destructive metabolic characterizations on in vitro cancer cells and organotypic tumor models for therapeutic studies in an easy-to-access way, we report a highly portable optical spectroscopic assay for simultaneous measurement of glucose uptake and mitochondrial function on various cancer models with high sensitivity. Well-established breast cancer cell lines (MCF-7 and MDA-MB-231) were used to validate the optical spectroscopic assay for metabolic characterizations, while fresh tumor samples harvested from both animals and human cancer patients were used to test the feasibility of our optical metabolic assay for non-destructive measurement of key metabolic parameters on organotypic tumor slices. Our optical metabolic assay captured that MCF-7 cells had higher mitochondrial metabolism, but lower glucose uptake compared to the MDA-MB-231 cells, which is consistent with our microscopy imaging and flow cytometry data, as well as the published Seahorse Assay data. Moreover, we demonstrated that our optical assay could non-destructively measure both glucose uptake and mitochondrial metabolism on the same cancer cell samples at one time, which remains challenging by existing metabolic tools. Our pilot tests on thin fresh tumor slices showed that our optical assay captured increased metabolic activities in tumors compared to normal tissues. Our non-destructive optical metabolic assay provides a cost-effective way for future longitudinal therapeutic studies using patient-derived organotypic fresh tumor slices through the lens of tumor energetics, which will significantly advance translational cancer research.

Non-contact optical spectroscopy for tumor-sensitive diffuse reflectance and fluorescence measurements on murine subcutaneous tissue models: Monte Carlo modeling and experimental validations.

Fiber-optic probes are commonly used in biomedical optical spectroscopy platforms for light delivery and collection. At the same time, it was reported that the inconsistent probe-sample contact could induce significant distortions in measured optical signals, which consequently cause large analysis errors. To address this challenge, non-contact optical spectroscopy has been explored for tissue characterizations. However, existing non-contact optical spectroscopy platforms primarily focused on diffuse reflectance measurements and may still use a fiber probe in which the probe was imaged onto the tissue surface using a lens, which serves as a non-contact probe for the measurements. Here, we report a fiber-probe-free, dark-field-based, non-contact optical spectroscopy for both diffuse reflectance and fluorescence measurements on turbid medium and tissues. To optimize the system design, we developed a novel Monte Carlo method to simulate such a non-contact setup for both diffuse reflectance and fluorescence measurements on murine subcutaneous tissue models with a spherical tumor-like target. We performed Monte Carlo simulations to identify the most tumor-sensitive configurations, from which we found that both the depth of the light focal point in tissue and the lens numerical aperture would dramatically affect the system's tumor detection sensitivity. We then conducted tissue-mimicking phantom studies to solidify these findings. Our reported Monte Carlo technique can be a useful computational tool for designing non-contact optical spectroscopy systems. Our non-contact optical setup and experimental findings will potentially offer a new approach for sensitive optical monitoring of tumor physiology in biological models using a non-contact optical spectroscopy platform to advance cancer research.

Change in economic burden of diarrhoea in children under-five in Bangladesh: 2007 vs. 2018.

Background: In Bangladesh, diarrhoea in children under-five is a major public health problem with cost implications. Although under-five diarrhoea mortality and morbidity have declined from 2007 to 2018, change in the economic burden is unknown. This study determined the change in the societal economic burden of under-five diarrhoea in Bangladesh comparing 2007 to 2018. Methods: A prevalence-based, retrospective cost analysis was conducted from a societal perspective, including costs to households, providers, and economic loss from premature deaths. Data were obtained from the previous cost of illness studies, government reports, and international databases. Direct costs for treatment were estimated by the bottom-up costing approach. Indirect costs on the loss of productivity of caretakers and loss from premature deaths were calculated by the human capital method. Total costs were presented in both local currency (Bangladeshi Taka (BDT)) and US dollars (US$)) in 2018 price. Sensitivity analyses were conducted to assess the robustness of the input parameters. Results: A 36.4% reduction was found on the economic burden of under-five diarrhoea when comparing 2007 and 2018; US$1 209 million (95% CI = 1066 million-1299 million) for 2007 and US$769 million (95% CI = 484 million-873 million) for 2018. Economic loss from premature deaths imposed the highest costs (2007 = 66%, 2018 = 66% of all) followed by indirect costs on the loss of productivity of caretakers (2007 = 21%, 2018 = 26%) and direct medical costs (2007 = 13%, 2018 = 8%). Conclusions: Societal costs from diarrhoeal diseases were reduced from 2007 to 2018 in Bangladesh. However, the economic burden was equivalent to 0.29% of country's gross domestic product in 2018 and remains a challenge. The major contributor to the costs was premature mortality from diarrhoeal diseases. Premature deaths are still prevalent though they to a large extent are avoidable. To further limit the economic burden, under-five diarrhoea mortality and morbidity reduction should be accelerated.

Costs of services and funding gap of the Bangladesh National Tuberculosis Control Programme 2016-2022: An ingredient based approach.

BACKGROUND: Bangladesh National Tuberculosis (TB) Control Programme (NTP) has deployed improved diagnostic technologies which may drive up the programme costs. We aimed to estimate the supply-side costs associated with the delivery of the NTP and the funding gap between the cost of implementation and available funding for the Bangladesh NTP. METHODS: An ingredient-based costing approach was applied using WHO's OneHealth Tool software. We considered 2016, as the base year and projected cost estimates up to 2022 using information on NTP planned activities. Data were collected through consultative meetings with experts and officials/managers, review of documents and databases, and visits to five purposively selected TB healthcare facilities. The estimated costs were compared with the funds allocated to the NTP between 2018 and 2022 to estimate the funding gap. FINDINGS: The estimated total cost of NTP was US$ 49.22 million in 2016, which would increase to US$ 146.93 million in 2022. Human resources (41.1%) and medicines and investigations/ supplies (38.0%) were the major two cost components. Unit costs were highest for treating extensively drug-resistant TB at US$ 7,422.4 in 2016. Between 2018-2022, NTP would incur US$ 536.8 million, which is US$ 235.18 million higher than the current allocation for NTP. CONCLUSION: Our results indicated a funding gap associated with the NTP in each of the years between 2018-2022. Policy planners should advocate for additional funding to ensure smooth delivery of TB services in the upcoming years. The cost estimates of TB services can also be used for planning and budgeting for delivering TB services in similar country contexts.

The Prevalence of Multi-Drug Resistant Salmonella typhi Isolated From Blood Sample.

Typhoid is a major public health concern. Even though antibiotics are usually used to treat typhoid fever, the spread of multi drug resistant Salmonella typhi is making antibiotics much less effective. This study was conducted to assess the prevalence of multidrug-resistant Salmonella typhi from the clinical samples. During this study, 154 blood samples of suspected typhoid patients were collected from the hospital and diagnostic center located in Chattogram City, Bangladesh. Isolation and identification of Salmonella typhi was done by both biochemical tests. PCR analysis was also done for the confirmation of biochemical result. Antimicrobial susceptibility test was performed according to the Kirby-Bauer disk diffusion method against ampicillin, chloramphenicol, cefepime, cotrimoxazole, ceptriaxone, ciprofloxacin, nalidixic acid, and azithtomycin. Out of 154, 21 (13.64%) isolates were identified as Salmonella typhi and the prevalence of typhoid in Chattogram, Bangladesh was 13.64% (n = 21). It was also found that children under the age of 5 are the more vulnerable target of Salmonella typhi infection. Antibiotic resistance profiling revealed 85% isolates were Multi-Drug Resistant (MDR) and highest resistance was found in case of Nalidixic acid. Although, most of the isolated Salmonella typhi were MDR, first generation antibiotics Co-trimoxazile, Chloramphenicol, and Ampicillin were found effective against Salmonella typhi.

Low-cost compact optical spectroscopy and novel spectroscopic algorithm for point-of-care real-time monitoring of nanoparticle delivery in biological tissue models.

Objective: Real-time monitoring of nanoparticle delivery in biological models is essential to optimize nanoparticle-mediated therapies. However, few techniques are available for convenient real-time monitoring of nanoparticle concentrations in tissue samples. This work reported novel optical spectroscopic approaches for low-cost point-of-care real-time quantification of nanoparticle concentrations in biological tissue samples. Methods: Fiber probe measured diffuse reflectance can be described with a simple analytical model by introducing an explicit dependence on the reduced scattering coefficient. Relying on this, the changes on the inverse of diffuse reflectance are proportional to absorption change when the scattering perturbation is negligible. We developed this model with proper wavelength pairs and implemented it with both a standard optical spectroscopy platform and a low-cost compact spectroscopy device for near real-time quantification of nanoparticle concentrations in biological tissue models. Results: Both tissue-mimicking phantom and ex vivo tissue sample studies showed that our optical spectroscopic techniques could quantify nanoparticle concentrations in near real-time with high accuracies (less than 5% error) using only a pair of narrow wavelengths (530 nm and 630 nm). Conclusion: Novel low-cost point-of-care optical spectroscopic techniques were demonstrated for rapid accurate quantification of nanoparticle concentrations in tissue-mimicking medium and ex vivo tissue samples using optical signals measured at a pair of narrow wavelengths. Significance: Our methods will potentially facilitate real-time monitoring of nanoparticle delivery in biological models using low-cost point-of-care optical spectroscopy platforms, which will significantly advance nanomedicine in cancer research.