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The emergence of smart and nanobiosensor (NB) technologies has transformed the monitoring and management of bacterial infections. These developments offer remarkable accuracy and precision for detecting infectious pathogens. Smart artificial intelligence (AI)-assisted and NB-based methods are used as powerful tools in biomedicine for bacterial detection, combatting multidrug resistance, and diagnosing infections. In this study, we delve into the advancements in these technologies, focusing on AI-based techniques for NBs in detecting bacterial infections from 2019 to 2024. We analyze the contributions of machine learning and deep learning techniques to enhance performance and reliability. The new approaches to improve the effectiveness and versatility of antibacterial treatments are critically analyzed. Our study includes the observations of carbon nanoparticles that selectively target bacteria using photothermal properties and the production of hybrid hydrogel composites with capabilities. Furthermore, the study emphasizes the crucial significance of NBs in propelling the progress of diagnostic methods, biosensing technologies, and treatments, thereby transforming the healthcare industry and the way diseases are managed. In addition, we explore pathogen-based infections, bacterial diagnosis, and treatment using engineered NBs enhanced with various modalities such as electrochemistry, acoustics, electromagnetism, and photothermal resonance. Our comprehensive review highlights the potential and throws light on future research directions for effective management and control of bacterial infections.

A significant number of cameras regularly generate massive amounts of data, demanding hardware, time, and labor resources to acquire, process, and monitor. Asymmetric frames within videos pose a challenge to automatic summarization of videos, making it challenging to capture key content. Developments in computer vision have accelerated the seamless capture and analysis of high-resolution video content. Video summarization (VS) has garnered considerable interest due to its ability to provide concise summaries of lengthy videos. The current literature mainly relies on a reduced set of representative features implemented using shallow sequential networks. Therefore, this work utilizes an optimal feature-assisted visual intelligence framework for representative feature selection and summarization. Initially, the empirical analysis of several features is performed, and ultimately, we adopt a fine-tuning InceptionV3 backbone for feature extraction, deviating from conventional approaches. Secondly, our strategic encoder–decoder module captures complex relationships with five convolutional blocks and two convolution transpose blocks. Thirdly, we introduced a channel attention mechanism, illuminating interrelations between channels and prioritizing essential patterns to grasp complex refinement features for final summary generation. Additionally, comprehensive experiments and ablation studies validate our framework's exceptional performance, consistently surpassing state-of-the-art networks on two benchmarks (TVSum and SumMe) datasets. [ABSTRACT FROM AUTHOR]

Background: In developing countries, data on the applicability of existing models to predict retinopathy of prematurity (ROP) are scarce. The study aimed to validate the Alexandria ROP (Alex-ROP) and high-grade Alex-ROP (Hg Alex-ROP) models retrospectively to identify treatable ROP in a cohort of preterm infants in Saudi Arabia. Materials and Methods: We reviewed and included the records of 281 infants born prematurely in 2015–2021. We recorded the infants' demographics, gestational age at birth (GA), birth weight (BW), and serial weight measurements (day 7, 14, 21, and 28). We determined whether the included met the Alex-ROP and Hg Alex-ROP detection criteria for treatable or any-stage ROP and calculated the specificity, sensitivity, negative and positive predictive values, and accuracy. Results: The median BW and GA was 1095 g (range: 426–1920 g) and 29 weeks (range: 23–36 weeks), respectively. ROP developed in 112 infants, of which 30 cases were treatable. The Alex-ROP sensitivity for correctly predicting any-stage ROP and treatable ROP was 77.7% and 80.0%, respectively, and its specificity for predicting any-stage ROP and treatable ROP was 49.7% and 41%, respectively. The Hg Alex-ROP had 36.6% and 50.0% sensitivity for detecting any-stage ROP and treatable ROP, respectively, and its specificity for detecting any-stage ROP and treatable ROP was 83.4% and 78.5%, respectively. Conclusion: Previously published accuracy parameters were not reproducible in this cohort and a significant number of children requiring treatment would have been missed if the Alex-ROP or Hg Alex-ROP were applied. [ABSTRACT FROM AUTHOR]

Simple Summary: Pyruvate decarboxylase (PDC) is a key enzyme involved in ethanol fermentation, a process for the production of biofuels. Thermostable and oxygen-stable PDC activity is highly desirable for biotechnological applications at high temperatures. The enzymes from the thermoacidophiles Saccharolobus (formerly Sulfolobus) solfataricus (Ss, Topt = 80 °C) and Sulfolobus acidocaldarius (Sa, Topt = 80 °C) were purified and characterized, and their biophysical and biochemical properties were determined comparatively. The purified enzymes were CoA-dependent and thermostable. There was no loss of activity in the presence of oxygen. In conclusion, both thermostable SsPDC and SaPDC catalyze the CoA-dependent production of acetaldehyde from pyruvate in the presence of oxygen. Pyruvate decarboxylase (PDC) is a key enzyme involved in ethanol fermentation, and it catalyzes the decarboxylation of pyruvate to acetaldehyde and CO2. Bifunctional PORs/PDCs that also have additional pyruvate:ferredoxin oxidoreductase (POR) activity are found in hyperthermophiles, and they are mostly oxygen-sensitive and CoA-dependent. Thermostable and oxygen-stable PDC activity is highly desirable for biotechnological applications. The enzymes from the thermoacidophiles Saccharolobus (formerly Sulfolobus) solfataricus (Ss, Topt = 80 °C) and Sulfolobus acidocaldarius (Sa, Topt = 80 °C) were purified and characterized, and their biophysical and biochemical properties were determined comparatively. Both enzymes were shown to be heterodimeric, and their two subunits were determined by SDS-PAGE to be 37 ± 3 kDa and 65 ± 2 kDa, respectively. The purified enzymes from S. solfataricus and S. acidocaldarius showed both PDC and POR activities which were CoA-dependent, and they were thermostable with half-life times of 2.9 ± 1 and 1.1 ± 1 h at 80 °C, respectively. There was no loss of activity in the presence of oxygen. Optimal pH values for their PDC and POR activity were determined to be 7.9 and 8.6, respectively. In conclusion, both thermostable SsPOR/PDC and SaPOR/PDC catalyze the CoA-dependent production of acetaldehyde from pyruvate in the presence of oxygen. [ABSTRACT FROM AUTHOR]

Background: Gastroesophageal reflux disease is one of the most common complaints in the medical field. Increased acid production and relaxation of the lower esophageal sphincter are considered the most common causes of this disease. Patients could present with epigastric pain that can be radiated to the left shoulder and can lead to a similar presentation of cardiac disease. Treatment could be nonpharmacological like weight loss and lifestyle modifications or pharmacological like given PPI other medications or surgical for refractory cases. Objective: The objective of this review is to discuss Gastro-esophageal reflux disease, different presentations, and management plans with great details. Method: We searched the PubMed database looking for relevant articles to the topic using Mesh terms, "Gastro-esophageal reflux disease". Conclusion: Gastro-esophageal reflux disease is a common condition, the early diagnosis, and treatment, will not only treat your patient, and rather, it will prevent serious complications like malignancy. [ABSTRACT FROM AUTHOR]

Background: Dispensing of antibiotics without a prescription (DAwP) has been widely practised among community pharmacies in Saudi Arabia despite being illegal. However, in May 2018, the law and regulations were enforced alongside fines. Consequently, we wanted to evaluate the impact of these changes.Methods: A study was conducted among 116 community pharmacies in two phases. A pre-law enforcement phase between December 2017 and March 2018 and a post-law enforcement phase one year later. Each phase consisted of a cross-sectional questionnaire-based survey and a simulated client method (SCM) approach. In the SCM, clients presented with either pharyngitis or urinary tract infections (UTI). In SCM, for each phase, all 116 pharmacies were visited with one of the scenarios.Results: Before the law enforcement, 70.7% of community pharmacists reported that DAwP was common with 96.6% and 87.7% of participating pharmacies dispensed antibiotics without a prescription for pharyngitis and UTI respectively. After the law enforcement, only 12.9% reported that DAwP is still a common practice, with only 12.1% and 5.2% dispensing antibiotics without prescriptions for pharyngitis and UTI respectively.Conclusion: law enforcement was effective. However, there is still further scope for improvement. This could include further educational activities with pharmacists, physicians and the public. [ABSTRACT FROM AUTHOR]

Background: Cassava is a high-carbohydrate crop that is at risk of viral infections. The production rate and quality of cassava crops are affected by several diseases. However, the manual identification of diseases is challenging and requires considerable time because of the lack of field professionals and the limited availability of clear and distinct information. Consequently, the agricultural management system is seeking an efficient and lightweight method that can be deployable to edged devices for detecting diseases at an early stage. To address these issues and accurately categorize different diseases, a very effective and lightweight framework called CDDNet has been introduced. We used MobileNetV3Small framework as a backbone feature for extracting optimized, discriminating, and distinct features. These features are empirically validated at the early intermediate stage. Additionally, we modified the soft attention module to effectively prioritize the diseased regions and enhance significant cassava plant disease-related features for efficient cassava disease detection., Results: Our proposed method achieved accuracies of 98.95%, 97.03%, and 98.25% on Cassava Image Dataset, Cassava Plant Disease Merged (2019-2020) Dataset, and the newly created Cassava Plant Composite Dataset, respectively. Furthermore, the proposed technique outperforms previous state-of-the-art methods in terms of accuracy, parameter count, and frames per second values, ultimately making the proposed CDDNet the best one for real-time processing., Conclusion: Our findings underscore the importance of a lightweight and efficient technique for cassava disease detection and classification in a real-time environment. Furthermore, we highlight the impact of modified soft attention on model performance. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry., (© 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.)

Background: Hypertension and thyroid nodules (TNs) are common medical problems that are increasing in prevalence globally. Hence, we conducted this study to assess the prevalence and associated predictors of hypertension in adult patients with TNs at the Royal Commission Hospital, Kingdom of Saudi Arabia (KSA)., Methods: A retrospective study was conducted between 1 January 2015 and 31 December 2021. Patients with documented TNs based on the Thyroid Imaging Reporting and Data System (TI-RADS) were recruited to assess the prevalence and associated risk factors for hypertension., Result: Three hundred ninety-one patients with TNs were recruited for this study. The median (interquartile range, IQR) age was 46.00 (20.0) years, and 332 (84.9%) of the patients were females. The median (IQR) body mass index (BMI) was 30.26 (7.71) kg/m 2 . There was a high prevalence of hypertension (22.5%) in adult patients with TNs. In the univariate analysis, there were significant associations between diagnosed hypertension in patients with TNs and age, sex, diabetes mellitus (DM), bronchial asthma, triiodothyronine (FT3), total cholesterol and high-density lipoprotein (HDL). In the multivariate analysis, age (OR = 1.076 [95% CI 1.048 - 1.105]), sex (OR = 2.28 [95% CI 1.132 - 4.591]), DM (OR = 0.316 [95% CI 0.175 - 0.573]) and total cholesterol levels (OR = 0.820 [95% CI 0.694 - 0.969]) were significantly associated with hypertension., Conclusion: There is a high prevalence of hypertension in patients with TNs. Age, female sex, DM and elevated total cholesterol are significant predictors of hypertension in adult patients with TNs., Competing Interests: Competing Interest The authors declare that they have no competing interests.