Bacterobilia in acute cholecystitis: bile cultures\u27 isolates, antibiotic sensitivities and antibiotic usage. a study on a Pakistani population.

Abstract Acute cholecystitis is one of the most common acute surgical conditions. Laparoscopic cholecystectomy remains the mainstay of treatment. In patients managed non-operatively, antibiotics play an important role in the treatment of cholecystitis. The current retrospective observational study was conducted at a tertiary care hospital in Karachi, and comprised medical records of patients admitted between 2008 and 2014with acute cholecystitis and in whom bile cultures were obtained. Of the 509 patients with a mean age of 51.15 ± 13.4years, early laparoscopic cholecystectomy (within 72hours) was performed on 473(92.9%) cases, while the rest underwent percutaneous cholecystostomy. Bile cultureswere positive in 171(33.6%) patients. Predominantly gram-negative organisms were isolated among a total of 137(27%), with E.coli 63(46%) being the most commonly isolated organism. Of the gram-positive organism, enterococcus 11(8%) was the most common. Antibiotic sensitivities were determined.Based on our findings gram-negative coverage alone should be sufficient in our segment of the population

Digital Predistortion Based Experimental Evaluation of Optimized Recurrent Neural Network for 5G Analog Radio Over Fiber Links

In the context of Enhanced Remote Area Communications (ERAC), Radio over Fiber (RoF) technology plays a crucial role in extending reliable connectivity to underserved and remote areas. This paper explores the significance of fifth-generation (5G) Digital Predistortion (DPD) role in mitigating non-linearities in Radio over Fiber (RoF) systems for enhancing communication capabilities in remote regions. The seamless integration of RoF and 5G technologies requires robust linearization techniques to ensure high-quality signal transmission. In this paper, we propose and exhibit the effectiveness of a machine learning (ML)-based DPD method for linearizing next-generation Analog Radio over Fiber (A-RoF) links within the 5G landscape. The study investigates the use of an optimized recurrent neural network (ORNN) based DPD experimentally on a multiband 5G new radio (NR) A-RoF system while maintaining low complexity. The ORNN model is evaluated using flexible-waveform signals at 2.14 GHz and 5G NR signals at 10 GHz transmitted over a 10 km fiber length. The proposed ORNN-based machine learning approach is optimized and is compared with conventional generalized memory polynomial (GMP) model and canonical piecewise linearization (CPWL) methods in terms of Adjacent Channel Power Ratio (ACPR), Error Vector Magnitude (EVM), and in terms of computation complexity including, storage, time and memory consumption. The findings demonstrate that the proposed ORNN model reduces EVM to below 2% as compared to 12% for non-compensated cases while ACPR is reduced by 18 dBc, meeting 3GPP limits

Bacterobilia in acute cholecystitis: bile cultures\u27 isolates

Abstract Acute cholecystitis is one of the most common acute surgical conditions. Laparoscopic cholecystectomy remains the mainstay of treatment. In patients managed non-operatively, antibiotics play an important role in the treatment of cholecystitis. The current retrospective observational study was conducted at a tertiary care hospital in Karachi, and comprised medical records of patients admitted between 2008 and 2014with acute cholecystitis and in whom bile cultures were obtained. Of the 509 patients with a mean age of 51.15 ± 13.4years, early laparoscopic cholecystectomy (within 72hours) was performed on 473(92.9%) cases, while the rest underwent percutaneous cholecystostomy. Bile cultureswere positive in 171(33.6%) patients. Predominantly gram-negative organisms were isolated among a total of 137(27%), with E.coli 63(46%) being the most commonly isolated organism. Of the gram-positive organism, enterococcus 11(8%) was the most common. Antibiotic sensitivities were determined.Based on our findings gram-negative coverage alone should be sufficient in our segment of the populatio

AI in drug discovery and its clinical relevance

The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.  Other InformationPublished in:HeliyonLicense: https://creativecommons.org/licenses/by/4.0/See article on publisher's website: https://doi.org/10.1016/j.heliyon.2023.e17575 </p

Latest research trends in gait analysis using wearable sensors and machine learning: a systematic review

Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications

Association of Socio-Demographic and Clinicopathological Risk Factors with Oral Cancers: A 19-Year Retrospective Study

Objective: To determine the association of socio-demographic and clinic-pathological risk factors with oral cancer in Kelantan, Malaysia. Material and Methods: A 19-year cross-sectional survey was performed in Hospital Universiti Sains Malaysia (HUSM), Malaysia. Medical record of 301 oral cancer patients was retrieved from the Medical Records office. Results: The majority of the oral cancer cases were male (62.8%), non-smokers (57.5%), non-alcohol consumers (83.4%), non-betel quid chewers (96.7%), and belonged to Malay ethnicity (68.8%). At the time of diagnosis, most of the patients were at stage II (38.9%). Approximately one-third (30.6%) of the total OC patients experienced loco-regional/distant metastasis, whereas no metastasis was detected in around two-thirds of cases (69.4%). A combination of surgery and radiotherapy was the most commonly employed treatment modality (27.2%). At the time of this study, the survival status of most of the patients was alive (69.1%). The most frequently encountered oral cancer in the Kelantanese population was oral squamous cell carcinoma (70.1%), with the tongue being the most frequently involved oral cavity site (35.5%). Conclusion: More than three-fourths of the cases were alive at follow-up, which included the cases that did not undergo any form of treatment

Association of Socio-Demographic and Clinicopathological Risk Factors with Oral Cancers: A 19-Year Retrospective Study

Objective: To determine the association of socio-demographic and clinic-pathological risk factors with oral cancer in Kelantan, Malaysia. Material and Methods: A 19-year cross-sectional survey was performed in Hospital Universiti Sains Malaysia (HUSM), Malaysia. Medical record of 301 oral cancer patients was retrieved from the Medical Records office. Results: The majority of the oral cancer cases were male (62.8%), non-smokers (57.5%), non-alcohol consumers (83.4%), non-betel quid chewers (96.7%), and belonged to Malay ethnicity (68.8%). At the time of diagnosis, most of the patients were at stage II (38.9%). Approximately one-third (30.6%) of the total OC patients experienced loco-regional/distant metastasis, whereas no metastasis was detected in around two-thirds of cases (69.4%). A combination of surgery and radiotherapy was the most commonly employed treatment modality (27.2%). At the time of this study, the survival status of most of the patients was alive (69.1%). The most frequently encountered oral cancer in the Kelantanese population was oral squamous cell carcinoma (70.1%), with the tongue being the most frequently involved oral cavity site (35.5%). Conclusion: More than three-fourths of the cases were alive at follow-up, which included the cases that did not undergo any form of treatment

Association of Stress, Knowledge Management, and Change with Organizational Effectiveness in Education Sector of Pakistan

The purpose of this study is to analyze the impact of organizational stress, knowledge management, and organizational change on organizational effectiveness. A valid questionnaire was distributed to administrative staff and faculty members of different educational institutes. 100 questionnaires were distributed in public and private educational sectors. 75 complete questionnaires were received at response rate of 75%. A non probability random sampling technique was used to select the sample. Pearson&rsquo;s moment correlation and linear regression was applied to study the relationship between organizational stress, knowledge management, organizational change and organizational effectiveness. Results show significant relationship of factors and positive impact on organizational effectiveness. This research also discusses practical implicatios and research limitations.&nbsp

Anaerobic Biodegradability and Biomethanation Potential of Fruit-Vegetable Wastes at Sindh, Pakistan

The urban environment of every city of Pakistan has been degraded because of open dumping and burning of organic wastes along with other wastes. The focus of this study was to evaluate the fruit and vegetable wastes for methane generation through biomethanation process. After collection, various parameters such as alkalinity, volatile fatty acids, pH, lignin content, moisture content, total solids, volatile solids, fixed carbon, and elements (C, H, N, O, S) of waste samples were determined by using standard methodology. Anaerobic biodegradability of fruit and vegetable wastes was observed from 54 to 77% and from 59 to 87% along with their methane generation potential in the range of 258-367 NmL /gmVS and 274-407 NmL/gmVS, respectively. Further, the effect of feedstock to inoculum ratio was studied. The result of that showed that lower methane potential at a higher ratio and vice versa was observed. It was concluded that at a lower feedstock to inoculum ratio, fruit as well as vegetable wastes become more feasible for the biomethanation process. The study recommends that the conversion of fruit and vegetable wastes into methane gas by anaerobic digestion plays a significant role to save urban environment of the country

Synthesis, characterization and application of novel MnO and CuO impregnated biochar composites to sequester arsenic (As) from water: Modeling, thermodynamics and reusability

© 2020 Elsevier B.V. The present study aimed at enhancing the adsorption potential of novel nanocomposites of Sesbania bispinosa biochar (SBC) with copper oxide (SBC/CuO) and manganese oxide nanoparticles (SBC/MnO) for the efficient and inexpensive removal of environmentally concerned contaminant arsenic (As) from contaminated water at batch scale. The scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, energy dispersive X-ray (EDX), X-ray diffraction (XRD) and point of zero charge (PZC) analyses proved successful impregnation of the metallic nanoparticles on SBC surface. The results revealed the maximum As removal (96 %) and adsorption (12.47 mg/g) by SBC/CuO composite at 10 mg As/L, optimum pH-4, dose 1.0 g/L and ambient temperature (25 ± 1.5 °C) as compared with SBC (7.33 mg/g) and SBC/MnO (7.34 mg/g). Among four types of adsorption isotherms, Freundlich isotherm demonstrated best fit with R2 \u3e 0.997. While pseudo second-order kinetic model revealed better agreement with kinetic experimental data as matched with other kinetic models. The thermodynamic results depicted that As adsorption on the as-synthesized adsorbents was endothermic and spontaneous in nature with increased randomness. The SBC/CuO displayed excellent reusability and stability over four adsorption/desorption cycles and proved that the as-synthesized SBC/CuO composite may be the efficient adsorbent for practical removal of As from contaminated water