TACAN Is an Ion Channel Involved in Sensing Mechanical Pain.

Mechanotransduction, the conversion of mechanical stimuli into electrical signals, is a fundamental process underlying essential physiological functions such as touch and pain sensing, hearing, and proprioception. Although the mechanisms for some of these functions have been identified, the molecules essential to the sense of pain have remained elusive. Here we report identification of TACAN (Tmem120A), an ion channel involved in sensing mechanical pain. TACAN is expressed in a subset of nociceptors, and its heterologous expression increases mechanically evoked currents in cell lines. Purification and reconstitution of TACAN in synthetic lipids generates a functional ion channel. Finally, a nociceptor-specific inducible knockout of TACAN decreases the mechanosensitivity of nociceptors and reduces behavioral responses to painful mechanical stimuli but not to thermal or touch stimuli. We propose that TACAN is an ion channel that contributes to sensing mechanical pain.

Measuring occupancies of the nucleosome and nucleosome-interacting factors in vivo in Saccharomyces cerevisiae genome-wide.

Nucleosomes are the repeating units of chromatin. The presence of nucleosomes poses a major impediment to all DNA-dependent processes. As a result, access to DNA in chromatin is dynamically regulated by many factors, including ATP-dependent chromatin remodeling complexes. Digestion of chromatin by micrococcal nuclease (MNase) followed by chromatin immunoprecipitation (ChIP) and sequencing can be leveraged to determine nucleosome occupancy, positioning, and the ability of chromatin interacting factors to alter chromatin accessibility. Here we describe the procedure for performing MNase and MNase ChIP-seq in detail.

Comparative transcriptomic and evolutionary analysis of FAD-like genes of Brassica species revealed their role in fatty acid biosynthesis and stress tolerance.

BACKGROUND: Fatty acid desaturases (FADs) are involved in regulating plant fatty acid composition by adding double bonds to growing hydrocarbon chain. Apart from regulating fatty acid composition FADs are of great importance, and are involved in stress responsiveness, plant development, and defense mechanisms. FADs have been extensively studied in crop plants, and are broadly classed into soluble and non-soluble fatty acids. However, FADs have not yet been characterized in Brassica carinata and its progenitors. RESULTS: Here we have performed comparative genome-wide identification of FADs and have identified 131 soluble and 28 non-soluble FADs in allotetraploid B. carinata and its diploid parents. Most soluble FAD proteins are predicted to be resided in endomembrane system, whereas FAB proteins were found to be localized in chloroplast. Phylogenetic analysis classed the soluble and non-soluble FAD proteins into seven and four clusters, respectively. Positive type of selection seemed to be dominant in both FADs suggesting the impact of evolution on these gene families. Upstream regions of both FADs were enriched in stress related cis-regulatory elements and among them ABRE type of elements were in abundance. Comparative transcriptomic data analysis output highlighted that FADs expression reduced gradually in mature seed and embryonic tissues. Moreover, under heat stress during seed and embryo development seven genes remained up-regulated regardless of external stress. Three FADs were only induced under elevated temperature whereas five genes were upregulated under Xanthomonas campestris stress suggesting their involvement in abiotic and biotic stress response. CONCLUSIONS: The current study provides insights into the evolution of FADs and their role in B. carinata under stress conditions. Moreover, the functional characterization of stress-related genes would exploit their utilization in future breeding programs of B. carinata and its progenitors.

Comparing the efficacy of EUS versus MRCP with ERCP as gold standard in patients presenting with partial biliary obstruction - finding a better diagnostic tool.

Objective: To evaluate the diagnostic accuracy of different imaging modalities in patients with partial biliary obstruction with no obvious aetiology on initial imaging. Methods: This is a prospective single-centre cohort study carried out at Pak Emirates Military Hospital, Rawalpindi from June 2019 to June 2021 with non-probability consecutive sampling. Patients with ages 16 to 75 years, presenting with partial biliary obstruction and undetermined aetiology on initial imaging (TUS and MRCP) were enrolled. EUS was performed for each of these patients and the case was regarded as "true positive" or "true negative" if the findings of imaging modality correlated to those of ERCP. ROC curve, sensitivity, specificity, PPV, NPV and AUC (with 95% confidence interval) were drawn for all the diagnostic tools using SPSS V. 21. Results: A total of 65 patients were enrolled over a period of two years with male to female ratio of 1.4:1. Forty-four patients had an intermediate risk of choledocholithiasis upon preliminary evaluation whereas, 48(74%) of the participants had CBD calculi or sludge confirmed upon subsequent ERCP. Trans-abdominal ultrasound showed the lowest sensitivity (29.2%), specificity (85%), NPV 12% and PPV 93% for diagnosing CBD calculi. This was followed by MRCP with a sensitivity of 37.5%, specificity of 100%, NPV of 36.2% and PPV of 100%. EUS showed the maximum diagnostic accuracy with AUC of 1.0 and a 100% sensitivity and specificity when compared with ERCP as gold standard. Conclusion: EUS is superior to MRCP in terms of diagnostic accuracy as minimally invasive diagnostic tool and EUS superiority is particularly relevant in patients with intermediate risk of choledocholithiasis.

A Machine-Learning-Based Robust Classification Method for PV Panel Faults.

Renewable energy resources have gained considerable attention in recent years due to their efficiency and economic benefits. Their proportion of total energy use continues to grow over time. Photovoltaic (PV) cell and wind energy generation are the least-expensive new energy sources in most countries. Renewable energy technologies significantly contribute to climate mitigation and provide economic benefits. Apart from these advantages, renewable energy sources, particularly solar energy, have drawbacks, for instance restricted energy supply, reliance on weather conditions, and being affected by several kinds of faults, which cause a high power loss. Usually, the local PV plants are small in size, and it is easy to trace any fault and defect; however, there are many PV cells in the grid-connected PV system where it is difficult to find a fault. Keeping in view the aforedescribed facts, this paper presents an intelligent model to detect faults in the PV panels. The proposed model utilizes the Convolutional Neural Network (CNN), which is trained on historic data. The dataset was preprocessed before being fed to the CNN. The dataset contained different parameters, such as current, voltage, temperature, and irradiance, for five different classes. The simulation results showed that the proposed CNN model achieved a training accuracy of 97.64% and a testing accuracy of 95.20%, which are much better than the previous research performed on this dataset.

Improvement in the Tracking Performance of a Maneuvering Target in the Presence of Clutter.

The proposed work uses fixed lag smoothing on the interactive multiple model-integrated probabilistic data association algorithm (IMM-IPDA) to enhance its performance. This approach makes use of the advantages of the fixed lag smoothing algorithm to track the motion of a maneuvering target while it is surrounded by clutter. The suggested method provides a new mathematical foundation in terms of smoothing for mode probabilities in addition to the target trajectory state and target existence state by including the smoothing advantages. The suggested fixed lag smoothing IMM-IPDA (FLs IMM-IPDA) method's root mean square error (RMSE), true track rate (TTR), and mode probabilities are compared to those of other recent algorithms in the literature in this study. The results clearly show that the proposed algorithm outperformed the already-known methods in the literature in terms of these above parameters of interest.

Towards Automatic License Plate Detection.

Automatic License Plate Detection (ALPD) is an integral component of using computer vision approaches in Intelligent Transportation Systems (ITS). An accurate detection of vehicles' license plates in images is a critical step that has a substantial impact on any ALPD system's recognition rate. In this paper, we develop an efficient license plate detecting technique through the intelligent combination of Faster R-CNN along with digital image processing techniques. The proposed algorithm initially detects vehicle(s) in the input image through Faster R-CNN. Later, the located vehicle is analyzed by a robust License Plate Localization Module (LPLM). The LPLM module primarily uses color segmentation and processes the HSV image to detect the license plate in the input image. Moreover, the LPLM module employs morphological filtering and dimension analysis to find the license plate. Detailed trials on challenging PKU datasets demonstrate that the proposed method outperforms few recently developed methods by producing high license plates detection accuracy in much less execution time. The proposed work demonstrates a great feasibility for security and target detection applications.

Risk of colorectal cancer in patients with diabetes mellitus: A Swedish nationwide cohort study.

BACKGROUND: Colorectal cancer (CRC) incidence is increasing among young adults below screening age, despite the effectiveness of screening in older populations. Individuals with diabetes mellitus are at increased risk of early-onset CRC. We aimed to determine how many years earlier than the general population patients with diabetes with/without family history of CRC reach the threshold risk at which CRC screening is recommended to the general population. METHODS AND FINDINGS: A nationwide cohort study (follow-up:1964-2015) involving all Swedish residents born after 1931 and their parents was carried out using record linkage of Swedish Population Register, Cancer Registry, National Patient Register, and Multi-Generation Register. Of 12,614,256 individuals who were followed between 1964 and 2015 (51% men; age range at baseline 0-107 years), 162,226 developed CRC, and 559,375 developed diabetes. Age-specific 10-year cumulative risk curves were used to draw conclusions about how many years earlier patients with diabetes reach the 10-year cumulative risks of CRC in 50-year-old men and women (most common age of first screening), which were 0.44% and 0.41%, respectively. Diabetic patients attained the screening level of CRC risk earlier than the general Swedish population. Men with diabetes reached 0.44% risk at age 45 (5 years earlier than the recommended age of screening). In women with diabetes, the risk advancement was 4 years. Risk was more pronounced for those with additional family history of CRC (12-21 years earlier depending on sex and benchmark starting age of screening). The study limitations include lack of detailed information on diabetes type, lifestyle factors, and colonoscopy data. CONCLUSIONS: Using high-quality registers, this study is, to our knowledge, the first one that provides novel evidence-based information for risk-adapted starting ages of CRC screening for patients with diabetes, who are at higher risk of early-onset CRC than the general population.

Personal History of Diabetes as Important as Family History of Colorectal Cancer for Risk of Colorectal Cancer: A Nationwide Cohort Study.

INTRODUCTION: Diabetes mellitus (DM) and colorectal cancer (CRC) share some risk factors, including lifestyle and metabolic disturbances. We aimed to provide in-depth information on the association of CRC risk, especially early-onset CRC, with DM, family history of CRC, and age at DM diagnosis. METHODS: A nationwide cohort study was conducted using Swedish family cancer data sets, inpatient, and outpatient registers (follow-up: 1964-2015), including all individuals born after 1931 and their parents (12,614,256 individuals; 559,375 diabetic patients; 162,226 CRC patients). RESULTS: DM diagnosis before the age of 50 years was associated with a 1.9-fold increased risk of CRC before the age of 50 years (95% CI for standardized incidence ratio: 1.6-2.3) vs 1.3-fold risk of CRC at/after the age of 50 years (1.2-1.4). DM diagnosis before the age of 50 years in those with a family history of CRC was associated with 6.9-fold risk of CRC before the age of 50 years (4.1-12) and 1.9-fold risk of CRC at/after the age of 50 years (1.4-2.5). Diabetic patients had a similar lifetime risk of CRC before the age of 50 years (0.4%, 95% CI: 0.3%-0.4%) to those with only a family history of CRC (0.5%, 0.5%-0.5%), double that of the population (0.2%, 0.2%-0.2%). DISCUSSION: Our large cohort with valid information on DM and family history of cancer showed that DM is associated with increased risk of CRC in a magnitude close to having family history of CRC. Associations of DM and CRC family history with increased CRC risk were most prominent in young adults. These findings warrant further studies on harms, benefits, and cost-effectiveness of CRC screening in patients with diabetes, especially type 2, at earlier ages than in the general population.

Distributed Target Tracking in Challenging Environments Using Multiple Asynchronous Bearing-Only Sensors.

In the multiple asynchronous bearing-only (BO) sensors tracking system, there usually exist two main challenges: (1) the presence of clutter measurements and the target misdetection due to imperfect sensing; (2) the out-of-sequence (OOS) arrival of locally transmitted information due to diverse sensor sampling interval or internal processing time or uncertain communication delay. This paper simultaneously addresses the two problems by proposing a novel distributed tracking architecture consisting of the local tracking and central fusion. To get rid of the kinematic state unobservability problem in local tracking for a single BO sensor scenario, we propose a novel local integrated probabilistic data association (LIPDA) method for target measurement state tracking. The proposed approach enables eliminating most of the clutter measurement disturbance with increased target measurement accuracy. In the central tracking, the fusion center uses the proposed distributed IPDA-forward prediction fusion and decorrelation (DIPDA-FPFD) approach to sequentially fuse the OOS information transmitted by each BO sensor. The track management is carried out at local sensor level and also at the fusion center by using the recursively calculated probability of target existence as a track quality measure. The efficiency of the proposed methodology was validated by intensive numerical experiments.

A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements.

Target detection and tracking is important in military as well as in civilian applications. In order to detect and track high-speed incoming threats, modern surveillance systems are equipped with multiple sensors to overcome the limitations of single-sensor based tracking systems. This research proposes the use of information from RADAR and Infrared sensors (IR) for tracking and estimating target state dynamics. A new technique is developed for information fusion of the two sensors in a way that enhances performance of the data association algorithm. The measurement acquisition and processing time of these sensors is not the same; consequently the fusion center measurements arrive out of sequence. To ensure the practicality of system, proposed algorithm compensates the Out of Sequence Measurements (OOSMs) in cluttered environment. This is achieved by a novel algorithm which incorporates a retrodiction based approach to compensate the effects of OOSMs in a modified Bayesian technique. The proposed modification includes a new gating strategy to fuse and select measurements from two sensors which originate from the same target. The state estimation performance is evaluated in terms of Root Mean Squared Error (RMSE) for both position and velocity, whereas, track retention statistics are evaluated to gauge the performance of the proposed tracking algorithm. The results clearly show that the proposed technique improves track retention and and false track discrimination (FTD).

Adaptive Filtering on GPS-Aided MEMS-IMU for Optimal Estimation of Ground Vehicle Trajectory.

Fusion of the Global Positioning System (GPS) and Inertial Navigation System (INS) for navigation of ground vehicles is an extensively researched topic for military and civilian applications. Micro-electro-mechanical-systems-based inertial measurement units (MEMS-IMU) are being widely used in numerous commercial applications due to their low cost; however, they are characterized by relatively poor accuracy when compared with more expensive counterparts. With a sudden boom in research and development of autonomous navigation technology for consumer vehicles, the need to enhance estimation accuracy and reliability has become critical, while aiming to deliver a cost-effective solution. Optimal fusion of commercially available, low-cost MEMS-IMU and the GPS may provide one such solution. Different variants of the Kalman filter have been proposed and implemented for integration of the GPS and the INS. This paper proposes a framework for the fusion of adaptive Kalman filters, based on Sage-Husa and strong tracking filtering algorithms, implemented on MEMS-IMU and the GPS for the case of a ground vehicle. The error models of the inertial sensors have also been implemented to achieve reliable and accurate estimations. Simulations have been carried out on actual navigation data from a test vehicle. Measurements were obtained using commercially available GPS receiver and MEMS-IMU. The solution was shown to enhance navigation accuracy when compared to conventional Kalman filter.

Multisensor-Based Target-Tracking Algorithm with Out-of-Sequence-Measurements in Cluttered Environments.

A localization and tracking algorithm for an early-warning tracking system based on the information fusion of Infrared (IR) sensor and Laser Detection and Ranging (LADAR) is proposed. The proposed Kalman filter scheme incorporates Out-of-Sequence Measurements (OOSMs) to address long-range, high-speed incoming targets to be tracked by networked Remote Observation Sites (ROS) in cluttered environments. The Rauch⁻Tung⁻Striebel (RTS) fixed lag smoothing algorithm is employed in the proposed technique to further improve tracking accuracy, which, in turn, is used for target profiling and efficient filter initialization at the targeted platform. This efficient initialization increases the probability of target engagement by increasing the distance at which it can be effectively engaged. The increased target engagement range also reduces risk of any damage from debris of the engaged target. Performance of the proposed target localization algorithm with OOSM and RTS smoothing is evaluated in terms of root mean square error (RMSE) for both position and velocity, which accurately depicts the improved performance of the proposed algorithm in comparison with existing retrodiction-based OOSM filtering algorithms. The effects of assisted target state initialization at the targeted platform are also evaluated in terms of Time to Impact (TTI) and true track retention, which also depict the advantage of the proposed strategy.

Medication-induced fractures: Screening and treatment strategies.

Medication-induced osteoporosis leads to substantial fracture morbidity. With polypharmacy and the aging population in the United States, significant increases in medication-associated fractures are predicted. The most common medication to cause osteoporosis and increase fractures is glucocorticoids. Many other therapies, including loop diuretics, SGLT2 inhibitors, thiazolidinediones, proton pump inhibitors, selective serotonin reuptake inhibitors, heparin, warfarin, antiepileptics, aromatase inhibitors, anti-androgen therapies, gonadotropin-releasing hormone antagonists, and calcineurin inhibitors are associated with increased fracture risks. Here, we review the latest evidence for fracture risk for these medications and discuss fracture risk screening and management strategies.

Reimagining the ADR Alert Card: a novel approach to recurrence prevention in low-cost settings for adverse drug reactions.

OBJECTIVES: Adverse drug reactions (ADRs) are among the leading standalone causes of morbidity and hospitalisation and contribute substantially to an increase in healthcare expenditure. Repeat ADR events, although difficult to quantify, are a recognised problem that lead to preventable suffering for the patient. The current approaches for the prevention of ADR recurrence in low/middle-income countries range from inefficient to non-existent. There is very little literature that focuses on the preventability of ADRs in such settings. This study aimed to develop the ADR Alert Card, an economical innovation designed as a stop gap in preventing ADR recurrence, and to evaluate its utility by validating the system through input from medical professionals. METHODS: The ADR Alert Card was validated and registered with the Copyrights Office of the Government of India. To obtain the opinion of healthcare professionals and gauge the status quo in prevention of ADR recurrence, we conducted an online descriptive cross-sectional study over a period of 6 months. RESULTS: The survey received 218 responses. Demographics varied, ranging across different healthcare specialties and years of experience. Our study found that existing practice in ADR recurrence prevention was inadequate, and most healthcare workers were unaware of an alternative approach. Unique solutions were provided by the respondents, with the majority favouring a card format for preventing recurrence. CONCLUSIONS: After being introduced to the ADR Alert Card, there was an overwhelming consensus on the utility and practicality of this card in preventing ADR recurrence.

A DFT study of bandgap tuning in chloro-fluoro silicene.

The structural, electronic and optical properties of silicene and its derivatives are investigated in the present work by employing density functional theory (DFT). The Perdew-Burke-Ernzerhof generalized gradient approximation (PBE-GGA) is used as the exchange-correlation potential. Our results provide helpful insight for tailoring the band gap of silicene via functionalization of chlorine and fluorine. First, relaxation of all the materials is performed to obtain the appropriate structural parameters. Cl-Si showed the highest lattice parameter 4.31 Å value, while it also possesses the highest buckling of 0.73 Å among all the derivatives of silicene. We also study the electronic charge density, charge difference density and electrostatic potential, to check the bonding characteristics and charge transfer between Si-halides. The electronic properties, band structures and density of states (DOS) of all the materials are calculated using the PBE-GGA as well as the modified Becke-Johnson (mBJ) on PBE-GGA. Pristine silicene is found to have a negligibly small band gap but with the adsorption of chlorine and fluorine atoms, its band gap can be opened. The band gap of Cl-Si and F-Si is calculated to be 1.7 eV and 0.6 eV, respectively, while Cl-F-Si has a band gap of 1.1 eV. Moreover, the optical properties of silicene and its derivatives are explored, which includes dielectric constants ε1 and ε2, refractive indices n, extinction coefficients k, optical conductivity σ and absorption coefficients I. The calculated binding energies and phonon band structures confirm the stability of Cl-Si, Cl-F-Si, and F-Si. We also calculated the photocatalytic properties which show silicine has a good response to reduction, and the other materials to oxidation. A comparison of our current work to recent work in which graphene was functionalized with halides, is also presented and we observe that silicene is a much better alternative for graphene in terms of semiconductors and photovoltaics applications.

Towards sustainable water reuse: A critical review and meta-analysis of emerging chemical contaminants with risk-based evaluation, health hazard prediction and prioritization for assessment of effluent water quality.

Reuse of treated wastewater is necessary to address water shortages in a changing climate. Sustainability of wastewater reuse requires reducing the environmental impacts of contaminants of emerging concern (CECs), but it is being questioned as CECs are not regulated in the assessment of effluent water quality for reuse both nationally in Sweden and at the broader European Union level. There is also a lack of details in this topic on which CECs to be addressed and methodologies to be used for assessing their environmental impacts. A better understanding of the ecological risks and health hazards of CECs associated with wastewater reuse will assist in the development of effective regulations on water reuse, (inter)nationally, as well as related treatment/monitoring guidelines. This review provides a list of specific chemical CECs that hinder sustainable wastewater reuse, and also demonstrates a holistic quantitative methodology for assessing, scoring and prioritizing their associated ecological risks and health hazards posed to the environment and humans. To achieve this, we compile information and concentrations of a wide range of CECs (∼15 000 data entries) identified in Swedish effluent wastewater from domestic (blackwater, greywater, mixture of both) and municipal settings, and further perform a meta-analysis of their potentials for 14 risk and hazard features, consisting of ecological risk, environmental hazard, and human health hazard. The features are then scored against defined criteria including guideline values, followed by score ranking for prioritization. This finally produces a unique list of chemical CECs from high to low priority based on risk- and hazard-evaluations. Out of the priority chemicals, 30, mainly pharmaceuticals, had risk quotient ≥ 1, indicating ecological risk, 16 had environmental hazard being persistent and mobile, and around 60 resulted in positive predictions for at least four human health hazards (particularly skin sensitization, developmental toxicity, hepatoxicity, and carcinogenicity). The 10 highest-priority chemicals (final score 2.3-3.0 out of 4.0) were venlafaxine, bicalutamide, desvenlafaxine, diclofenac, amoxicillin, clarithromycin, diethyltoluamide, genistein, azithromycin, and fexofenadine. Potential crop exposure to selected chemicals following one year of wastewater reuse for agricultural irrigation was also estimated, resulting in a range of 0.04 ng/kg (fluoxetine) to 1160 ng/kg (carbamazepine). Overall, our work will help focus efforts and costs on the critical chemicals in future (waste)water-related studies, such as, to evaluate removal efficiency of advanced treatment technologies and to study upstream source tracing (polluter-pays principle), and also in supporting policymakers to better regulate CECs for sustainable wastewater reuse in the future.

Phylogenomic curation of Ovate Family Proteins (OFPs) in the U's Triangle of Brassica L. indicates stress-induced growth modulation.

The Ovate Family Proteins (OFPs) gene family houses a class of proteins that are involved in regulating plant growth and development. To date, there is no report of the simultaneous functional characterization of this gene family in all members of U's Triangle of Brassica. Here, we retrieved a combined total of 256 OFP protein sequences and analyzed their chromosomal localization, gene structure, conserved protein motif domains, and the pattern of cis-acting regulatory elements. The abundance of light-responsive elements like G-box, MRE, and GT1 motif suggests that OFPs are sensitive to the stimuli of light. The protein-protein interaction network analysis revealed that OFP05 and its orthologous genes were involved in regulating the process of transcriptional repression through their interaction with homeodomain transcription factors like KNAT and BLH. The presence of domains like DNA binding 2 and its superfamily speculated the involvement of OFPs in regulating gene expression. The biotic and abiotic stress, and the tissue-specific expression analysis of the RNA-seq datasets revealed that some of the genes such as BjuOFP30, and BnaOFP27, BolOFP11, and BolOFP10 were highly upregulated in seed coat at the mature stage and roots under various chemical stress conditions respectively which suggests their crucial role in plant growth and development processes. Experimental validation of prominent BnaOFPs such as BnaOFP27 confirmed their involvement in regulating gene expression under salinity, heavy metal, drought, heat, and cold stress. The GO and KEGG pathway enrichment analysis also sheds light on the involvement of OFPs in regulating plant growth and development. These findings have the potential to serve as a forerunner for future studies in terms of functionally diverse analysis of the OFP gene family in Brassica and other plant species.

Development of a machine learning model to predict risk of development of COVID-19-associated mucormycosis.

Aim: The study aimed to identify quantitative parameters that increase the risk of rhino-orbito-cerebral mucormycosis, and subsequently developed a machine learning model that can anticipate susceptibility to developing this condition. Methods: Clinicopathological data from 124 patients were used to quantify their association with COVID-19-associated mucormycosis (CAM) and subsequently develop a machine learning model to predict its likelihood. Results: Diabetes mellitus, noninvasive ventilation and hypertension were found to have statistically significant associations with radiologically confirmed CAM cases. Conclusion: Machine learning models can be used to accurately predict the likelihood of development of CAM, and this methodology can be used in creating prediction algorithms of a wide variety of infections and complications.

Fungal infections caused by the Mucorales order of fungi usually target patients with a weakened immune system. They are usually also associated with abnormal blood sugar states, such as in diabetic patients. Recent work during the COVID-19 outbreak suggested that excessive steroid use and diabetes may be behind the rise in fungal infections caused by Mucorales, known as mucormycosis, in India, but little work has been done to see whether we can predict the risk of mucormycosis. This study found that these fungal infections need not necessarily be caused by Mucorales' species, but by a wide variety of fungi that target patients with weak immune systems. Secondly, we found that diabetes, breathing-assisting devices and high blood pressure states had associations with COVID-19-associated fungal infections. Finally, we were able to develop a machine learning model that showed high accuracy when predicting the risk of development of these fungal infections.

Comparison of Endoscopic Ultrasound and Transabdominal Ultrasound in the Detection of Gallbladder and Common Bile Duct Microlithiasis.

OBJECTIVE: Endoscopic ultrasonography (EUS) is an emerging method with a wide range of potential uses in gastroenterology, including the detection of bile duct stones and the identification of early ductal alterations in suspected patients. This study was designed to compare the diagnostic yield of EUS and transabdominal ultrasound (TUS) in the detection of gallbladder and common bile duct (CBD) microlithiasis. METHOD: Patients with biliary colic with normal initial TUS were the subjects of this prospective study. EUS scan was performed on all recruited patients and linear endoscopes were used for the EUS examination. Cholecystectomy and histological analysis were done in patients within two weeks after EUS revealing cholelithiasis whereas the cases of CBD stone/microlithiasis were confirmed by endoscopic retrograde cholangiopancreatography (ERCP). The mean values of all hematological characteristics were independently determined for males and females and then compared using Student's t-test. For statistical significance, a p-value of 0.05 or below was used. RESULTS: A total of 131 patients, including 77 females and 54 males, with a mean age of 38.41 ± 14.78 years were examined. All 78 (59.5%) individuals who had cholecystectomy were found to have gallstones or microlithiasis as successfully diagnosed by EUS. The sensitivity and specificity of EUS were 92.9% and 100%, respectively, for CBD stones and 98.8% and 100%, respectively, for the detection of gallbladder microlithiasis. The agreement between EUS and TUS was fair for CBD stones (κ = 0.214) and very weak for microlithiasis (κ = -0.093). CONCLUSION: EUS demonstrates a superior yield over TUS in detecting gallbladder stones and CBD microlithiasis, offering a more reliable diagnostic modality. LIMITATION: This was a single-center study.