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Background: Silicosis is known in industrial workers for centuries. Till recently, the mainstay of its diagnosis and progress was clinical examination of the respiratory system, pulmonary function test and chest radiography. Several biomarkers such as serum angiotensin converting enzyme (ACE) activity have been examined to determine the extent of silicosis. Objective: To elucidate the effect of age, gender, duration of exposure to silica dust, smoking habit, and pulmonary function status on the serum ACE activity among quartz stone workers without disease. Methods: A cross-sectional study was carried out on 134 (111 men and 14 women) workers of quartz stone crushing units were studied. Standard diagnostic criteria were used for diagnosing silicosis and tuberculosis. Pulmonary functions of the participants were also as-sessed. Results: The mean±SD age for participants was 26.1±6.3 years (26.6±6.3 for men and 21.9±4.3 for women). The mean±SD duration of exposure was 1.1±1.9 years. In the present study, only one case of silicosis and eight cases of tuberculosis were found. The mean±SD serum ACE levels for those with and without respiratory disease were 68.44±11.61, and 66.9±14.4 IU/L, respectively (p>0.05). Conclusion: We could not observe any association between serum ACE activity and age, gender, duration of exposure, smoking habits and pulmonary function status. However, elevated levels of serum ACE was found in a solitary case of silicosis. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to evaluate the efficacy and tolerability of a new fixed-dose combination (FDC) of telmisartan 40 mg + amlodipine 5 mg (T+A) compared with amlodipine 5-mg monotherapy (A) in adult Indian patients with stage II hypertension. Methods: This comparative, Phase III, 12-week, multicenter, prospective, randomized, double-blind study was conducted in Indian patients aged 18 to 65 years with established stage II hypertension. Patients were treated with oral FDC of T+A or A QD before breakfast for 12 weeks; blood pressure (BP) and heart rate were measured in the sitting position. Primary efficacy end points were reduction in clinical systolic BP (SBP)/ diastolic BP (DBP) from baseline to study end and number of responders (ie, patients who achieved target SBP/ DBP <130/<80 mm Hg) at end of study. Tolerability was assessed by treatment-emergent adverse events, identified using physical examination, laboratory analysis, and electrocardiography. Results: A total of 210 patients were enrolled in the study; 203 patients (143 men, 60 women) completed the study while 7 were lost to follow-up (4 patients in the T+A group and 3 in the A group) and considered with-drawn. At study end, statistically significant percentage reductions from baseline within groups and between groups were observed in SBP (T+A [-27.4%]; A [-16.6%]) and DBP (T+A [-20.1%]; A [-13.3%]) (all, P < 0.05). Response rates were 87.3% (89/102) in the T+A group and 69.3% (70/101) in the A group (P < 0.05). The prevalences of adverse events were not significantly different between the 2 treatment groups (T+A, 16.0% [17/106]; A, 15.4% [16/104]). Peripheral edema was reported in 8.5% patients (9/106) in the T+A group compared with 13.5% (14/104) in the A group, and cough was reported in 3.8% patients (4/106) in the T+A group and 1.0% (1/104) patients in the A group; these differences did not reach statistical significance. The incidences of headache, dizziness, and diarrhea were similar between the 2 groups. Conclusions: Among these Indian patients with stage II hypertension, the FDC of T+A was found to be significantly more effective, with regard to BP reductions, than A, and both treatments were well tolerated. [ABSTRACT FROM AUTHOR]

The use of novel active ingredients for the functional modification of chitosan nanoformulations has attracted global attention. In this study, chitosan has been functionalized via histidine to craft novel chitosan-histidine nanoformulation (C-H NF) using ionic gelation method. C-H NF exhibited elite physico-biochemical properties, influencing physiological and biochemical dynamics in Tomato. These elite properties include homogenous-sized nanoparticles (314.4 nm), lower PDI (0.218), viscosity (1.43 Cps), higher zeta potential (11.2 mV), nanoparticle concentration/ml (3.53 × 10 8 ), conductivity (0.046 mS/cm), encapsulation efficiency (53%), loading capacity (24%) and yield (32.17%). FTIR spectroscopy revealed histidine interaction with C-H NF, while SEM and TEM exposed its porous structure. Application of C-H NF to Tomato seedling and potted plants through seed treatment and foliar spray positively impacts growth parameters, antioxidant-defense enzyme activities, reactive oxygen species (ROS) content, and chlorophyll and nitrogen content. We claim that the histidine-functionalized chitosan nanoformulation enhances physico-biochemical properties, highlighting its potential to elevate biochemical and physiological processes of Tomato plant., (© 2024. The Author(s).)

Introduction: Enchondroma is a cartilaginous benign tumor of bone with the most common involvement of tubular bones of hands and feet. The triquetrum and pisiform are carpal bones and the enchondroma of the triquetrum and pisiform has not been reported in the literature to the best of our knowledge., Case Report: A 26-year-old male presented with swelling over his right wrist with painful terminal motion. Based on magnetic resonance imaging findings, diagnosis of enchondroma of triquetrum and pisiform was made. Bone excision and curettage were performed, and histopathology confirmed the diagnosis. After 2 years of follow-up, the patient has gained pain-free wrist movements post excision, and there are no signs of recurrence. The Modified Mayo Wrist Score of 75 (fair) improved to 100 (excellent)., Conclusion: The surgeon should always keep in mind the possibility of a tumor at the rare site and accurately diagnose the tumor with the help of imaging modalities and biopsy., Competing Interests: Conflict of Interest: Nil, (Copyright: © Indian Orthopaedic Research Group.)

This work successfully demonstrates a sustainable and environmentally friendly approach for synthesizing Semal-ZnO nanoparticles (NPs) using the aqueous leaf extract of Bombax ceiba L. These NPs exhibit an absorption peak at approximately 390 nm in the UV-visible spectrum and an energy gap (E g ) of 3.11 eV. Detailed analyses of the morphology and particle size using various spectroscopic and microscopic techniques, XRD, FE-SEM with EDS, and HR-TEM reveal crystallographic peaks attributable to the hexagonal phase, with an average crystal size of 17 nm. The Semal-ZnO NPs also exhibit a notable photocatalytic efficiency for degrading methylene blue (MB) and methyl orange (MO) under sunlight in different water samples collected from diverse natural sources, indicating that they are promising photocatalysts for environmental remediation. The photocatalytic efficiency of the biofabricated Semal-ZnO NPs is impressive, exhibiting a photodegradation rate of up to 99% for MB and 79% for MO in different water samples under exposure to sunlight. The novel phytofabricated Semal-ZnO NPs are thus a beacon of hope for the environment, with their desirable photocatalytic efficiency, pseudo-first-order kinetics, and ability to break down noxious dye pollutants in various aquatic environments., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Lal, Gour, Dave, Singh, Yadav, Khan, Jain, Agarwal, Sharma and Sharma.)

Fluorescence spectra of Pr 3+ doped CdS nanoparticles, synthesized by chemical precipitation method, have been recorded at room temperature. The synthesized particles are nearly spherical shaped and the grain size is decreased with the increase in Pr 3+ concentration. The chemical identity of the nanoparticles was confirmed by EDAX spectrum, the absorption peaks was confirmed by FTIR spectrum and then the recorded values were compared with the CIE diagram. The oscillator strengths of the 4f ↔ 4I transitions are parameterized in terms of three phenomenological Judd-Ofelt intensity parameters Ω λ (λ = 2, 4 and 6). Using the fluorescence data and these Ω λ parameters, theoretical and experimental study of various radiative properties viz., spontaneous emission probability (A), radiative life time , fluorescence branching ratio and stimulated emission cross-section were evaluated. The values of these parameters indicate that 3 P 0 → 3 H 4 transition can be considered to be good laser transition in the visible colour region. Also, excitation with 493 nm, leads to similar blue regions. The synthesized Pr 3+ doped CdS nanomaterials could be useful for sensing and detecting devices, particularly for temperature sensing measurement and bio-sensing detection., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Postpartum Depression Disorder (PPDD) is a prevalent mental health condition and results in severe depression and suicide attempts in the social community. Prompt actions are crucial in tackling PPDD, which requires a quick recognition and accurate analysis of the probability factors associated with this condition. This concern requires attention. The primary aim of our research is to investigate the feasibility of anticipating an individual's mental state by categorizing individuals with depression from those without depression using a dataset consisting of text along with audio recordings from patients diagnosed with PPDD. This research proposes a hybrid PPDD framework that combines Improved Bi-directional Long Short-Term Memory (IBi-LSTM) with Transfer Learning (TL) based on two Convolutional Neural Network (CNN) architectures, respectively CNN-text and CNN audio. In the proposed model, the CNN section efficiently utilizes TL to obtain crucial knowledge from text and audio characteristics, whereas the improved Bi-LSTM module combines written material and sound data to obtain intricate chronological interpersonal relationships. The proposed model incorporates an attention technique to augment the effectiveness of the Bi-LSTM scheme. An experimental analysis is conducted on the PPDD online textual and speech audio dataset collected from UCI. It includes textual features such as age, women's health tracks, medical histories, demographic information, daily life metrics, psychological evaluations, and 'speech records' of PPDD patients. Data pre-processing is applied to maintain the data integrity and achieve reliable model performance. The proposed model demonstrates a great performance in better precision, recall, accuracy, and F1-score over existing deep learning models, including VGG-16, Base-CNN, and CNN-LSTM. These metrics indicate the model's ability to differentiate among women at risk of PPDD vs. non-PPDD. In addition, the feature importance analysis demonstrates that specific risk factors substantially impact the prediction of PPDD. The findings of this research establish a basis for improved precision and promptness in assessing the risk of PPDD, which may ultimately result in earlier implementation of interventions and the establishment of support networks for women who are susceptible to PPDD., (© 2024. The Author(s).)

Skin cancer is a frequently occurring and possibly deadly disease that necessitates prompt and precise diagnosis in order to ensure efficacious treatment. This paper introduces an innovative approach for accurately identifying skin cancer by utilizing Convolution Neural Network architecture and optimizing hyperparameters. The proposed approach aims to increase the precision and efficacy of skin cancer recognition and consequently enhance patients' experiences. This investigation aims to tackle various significant challenges in skin cancer recognition, encompassing feature extraction, model architecture design, and optimizing hyperparameters. The proposed model utilizes advanced deep-learning methodologies to extract complex features and patterns from skin cancer images. We enhance the learning procedure of deep learning by integrating Standard U-Net and Improved MobileNet-V3 with optimization techniques, allowing the model to differentiate malignant and benign skin cancers. Also substituted the crossed-entropy loss function of the Mobilenet-v3 mathematical framework with a bias loss function to enhance the accuracy. The model's squeeze and excitation component was replaced with the practical channel attention component to achieve parameter reduction. Integrating cross-layer connections among Mobile modules has been proposed to leverage synthetic features effectively. The dilated convolutions were incorporated into the model to enhance the receptive field. The optimization of hyperparameters is of utmost importance in improving the efficiency of deep learning models. To fine-tune the model's hyperparameter, we employ sophisticated optimization methods such as the Bayesian optimization method using pre-trained CNN architecture MobileNet-V3. The proposed model is compared with existing models, i.e., MobileNet, VGG-16, MobileNet-V2, Resnet-152v2 and VGG-19 on the "HAM-10000 Melanoma Skin Cancer dataset". The empirical findings illustrate that the proposed optimized hybrid MobileNet-V3 model outperforms existing skin cancer detection and segmentation techniques based on high precision of 97.84%, sensitivity of 96.35%, accuracy of 98.86% and specificity of 97.32%. The enhanced performance of this research resulted in timelier and more precise diagnoses, potentially contributing to life-saving outcomes and mitigating healthcare expenditures., (© 2024. The Author(s).)

Virtual machine (VM) integration methods have effectively proven an optimized load balancing in cloud data centers. The main challenge with VM integration methods is the trade-off among cost effectiveness, quality of service, performance, optimal resource utilization and compliance with service level agreement violations. Deep Learning methods are widely used in existing research on cloud load balancing. However, there is still a problem with acquiring noisy multilayered fluctuations in workload due to the limited resource-level provisioning. The long short-term memory (LSTM) model plays a vital role in the prediction of server load and workload provisioning. This research presents a hybrid model using deep learning with Particle Swarm Intelligence and Genetic Algorithm ("DPSO-GA") for dynamic workload provisioning in cloud computing. The proposed model works in two phases. The first phase utilizes a hybrid PSO-GA approach to address the prediction challenge by combining the benefits of these two methods in fine-tuning the Hyperparameters. In the second phase, CNN-LSTM is utilized. Before using the CNN-LSTM approach to forecast the consumption of resources, a hybrid approach, PSO-GA, is used for training it. In the proposed framework, a one-dimensional CNN and LSTM are used to forecast the cloud resource utilization at various subsequent time steps. The LSTM module simulates temporal information that predicts the upcoming VM workload, while a CNN module extracts complicated distinguishing features gathered from VM workload statistics. The proposed model simultaneously integrates the resource utilization in a multi-resource utilization, which helps overcome the load balancing and over-provisioning issues. Comprehensive simulations are carried out utilizing the Google cluster traces benchmarks dataset to verify the efficiency of the proposed DPSO-GA technique in enhancing the distribution of resources and load balancing for the cloud. The proposed model achieves outstanding results in terms of better precision, accuracy and load allocation., (© 2024. The Author(s).)

Till now, oral squamous cell carcinoma (OSCC) is graded as well-differentiated, moderately-differentiated, poorly-differentiated, and undifferentiated. However, this grading does not have a prediction of the prognosis of the patient. Also, prognosis impacts lymph node metastases, surgical margins, and vascular invasions (neural invasion, muscular invasion, salivary gland invasion). The prognosis of lymph node metastases is significant, which affects the survival of the patients which is 50%. So, a dependable blood marker is needed for prognosis in OSCC patients with loco-regional and distant recurrence. Some factors can be assisted only after surgery and invasive techniques to check the prognosis of the disease. Despite the ease of examining the oral cavity, there is no practical approach for non-invasive screening and detecting cancer. As it is abrupt to use such invasive procedures from time to time, there is a need for nonsurgical and reliable techniques to assess the progression of tumors. Also, frozen sections are helpful during the intraoperative procedure to evaluate the lymph node metastases. An increase in the number of tumor cells through blood is a significant event in disease metastases toward the peripheral blood. Oral health impact assessment instruments could aid in determining the quality of life, and their usage in the initial stages of oral carcinoma could help physicians choose the best treatment option for enhancing the quality of life., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Sharma et al.)

Software-defined networking (SDN) has significantly transformed the field of network management through the consolidation of control and provision of enhanced adaptability. However, this paradigm shift has concurrently presented novel security concerns. The preservation of service path integrity holds significant importance within SDN environments due to the potential for malevolent entities to exploit network flows, resulting in a range of security breaches. This research paper introduces a model called "EnsureS", which aims to enhance the security of SDN by proposing an efficient and secure service path validation approach. The proposed approach utilizes a Lightweight Service Path Validation using Batch Hashing and Tag Verification, focusing on improving service path validation's efficiency and security in SDN environments. The proposed EnsureS system utilizes two primary techniques in order to validate service pathways efficiently. Firstly, the method utilizes batch hashing in order to minimize computational overhead. The proposed EnsureS algorithm enhances performance by aggregating packets through batches rather than independently; the hashing process takes place on each one in the service pathway. Additionally, the implementation of tag verification enables network devices to efficiently verify the authenticity of packets by leveraging pre-established trust relationships. EnsureS provides a streamlined and effective approach for validating service paths in SDN environments by integrating these methodologies. In order to assess the efficacy of the Proposed EnsureS, a comprehensive series of investigations were conducted within a simulated SDN circumstance. The efficacy of Proposed EnsureS was then compared to that of established methods. The findings of our study indicate that the proposed EnsureS solution effectively minimizes computational overhead without compromising on the established security standards. The implementation successfully reduces the impact of different types of attacks, such as route alteration and packet spoofing, increasing SDN networks' general integrity., (© 2023. Springer Nature Limited.)

Human monkeypox is a very unusual virus that can devastate society. Early identification and diagnosis are essential to treat and manage an illness effectively. Human monkeypox disease detection using deep learning models has attracted increasing attention recently. The virus that causes monkeypox may be passed to people, making it a zoonotic illness. The latest monkeypox epidemic has hit more than 40 nations. Computer-assisted approaches using Deep Learning techniques for automatically identifying skin lesions have shown to be a viable alternative in light of the fast proliferation and ever-growing problems of supplying PCR (Polymerase Chain Reaction) Testing in places with limited availability. In this research, we introduce a deep learning model for detecting human monkeypoxes that is accurate and resilient by tuning its hyper-parameters. We employed a mixture of convolutional neural networks and transfer learning strategies to extract characteristics from medical photos and properly identify them. We also used hyperparameter optimization strategies to fine-tune the Model and get the best possible results. This paper proposes a Yolov5 model-based method for differentiating between chickenpox and Monkeypox lesions on skin pictures. The Roboflow skin lesion picture dataset was subjected to three different hyperparameter tuning strategies: the SDG optimizer, the Bayesian optimizer, and Learning without Forgetting. The proposed Model had the highest classification accuracy (98.18%) when applied to photos of monkeypox skin lesions. Our findings show that the suggested Model surpasses the current best-in-class models and may be used in clinical settings for actual Human Monkeypox disease detection and diagnosis., (© 2023. Springer Nature Limited.)

Competing Interests: There are no conflicts of interest.

Competing Interests: There are no conflicts of interest.

The prompt use of an enamel surface covering reagent is advised to safeguard the dental restorative substance from mishaps. Therefore, it is of interest to assess the fluoride emitting capabilities of standard GIC, and Zirconomer cement together with surface coverings and without surface coverings. The conventional GIC cement was part of experimental category A while Zirconomer cement was part of category B. For every experimental categories, a set of sixty brass mould prototypes in the form of disc with dimensions: diameter (6±0.1mm) and thickness (2±0.1 mm) were created and subsequently covered with Teflon strip in accordance with the package recommendations. Also, for both experimental categories, such pellets were randomly allocated to three sub-categories of 20 each. For one category petroleum jelly was administered with a cotton bud and then delicately dried under airflow (A3 subcategory and B3 subcategory); for another sub-category G-Coat was laced through a micro-tip dispenser and light treated for twenty seconds (A2 subcategory and B2 subcategory); the rest 20 specimens were left without any coating (A1 subcategory and B1 subcategory). It was observed that in subcategory A1 and A3 there was continuous decline in emission of fluoride ion as the days progressed. However there was an increase in emission of fluoride in A2 subcategory on moving to day 5 from day 1. However, from day 5 onwards decline in fluoride emission was observed in A2 subcategory. It was concluded that both materials studied (GIC and Zirconomer) exhibited fluoride emission whether or not they were surface-coated for protection., (© 2023 Biomedical Informatics.)

Fluoride (F) in agricultural soil is increasing continuously due to injudicious application of F-laden fertilizers, causing global concern about fluorosis disease. The objective of the study was to assess F risk in humans due to soil ingestion, dermal contact, and particulate inhalation during various agricultural activities. The study also emphasized chemical fractionation, distribution, and geochemical understanding of high F incidence. Agricultural surface soil was sampled randomly from 5 km × 5 km square grids besides soil profile samples for studying the vertical distribution of F. Various F fractions in soil (1:1 soil:water ratio, calcium chloride extractable F, hot water soluble F, exchangeable F, Fe-Mn oxide bound F, organic matter bound F, residual F, and total F) were estimated using the sequential fractionation method. Multivariate geochemical analysis and soil F risk were also assessed in humans. The water soluble F (F 1:1 ) and CaCl 2 extractable F (F Ca ) varied between 0.11 to 6.73 mg kg -1 and 1.02 to 6.94 mg F kg -1 soil, respectively. Total fluoride (TF) however, ranged between 115 to 456 mg F kg -1 . A higher average of F Ca /TF moving down the soil profile indicated a propensity for F endemicity. Weathering, ion-exchange, alkalinity, and clay were found to control the soil geochemistry of the area. The F contamination index explained > 82% variance of F contamination, but the hazard quotient of F for an adult was found < 1, indicating no potential fluorosis risk in the area. This study is the first of its kind in India, where ecological risk due to F from agricultural soil was assessed in humans and will be a benchmark for future researchers., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background: Nail disorders account for about 10% of all dermatological conditions. Onychoscopy is useful not only for their diagnosis but also for assessing severity/progression and monitoring the response to therapy., Aims and Objectives: Describing dermoscopic features of nail disorders in patients reporting to the dermatology OPD of our tertiary care hospital and recording the sociodemographic profiles thereof., Materials and Methods: This cross-sectional observational study was carried out on 176 patients with effect from August 2019 to August 2021., Results: Males (99; 56.25%) outnumbered females (77; 43.75%); males: female: 1.28: 1; their mean age was 35.8 years. Fingernails were affected more oftener (84.09%) than toenails (38.64%). Onychomycosis, the commonest (58;32.95%) condition, revealed findings of aurora borealis pattern (75.86%), subungual hyperkeratosis (72.41%), and onycholysis with jagged edges and spikes (68.97%). The next frequent (32;18.18%) condition was nail psoriasis which revealed pits (81.25%); onycholysis (62.5%) and dilated globose nail fold vessels on capillaroscopy (25%)., Limitations: The small sample size proved inadequate for the evaluation of statistical significance in the less common conditions and the correlation of disease severity of many. Ideally, confirmatory diagnostic tests should have been done in every patient, as indicated. The magnification of our dermoscopy was 10X; 20- and 40X permit better capillaroscopy., Conclusions: Onychocopy can minimize the need for biopsy by highlighting subtle changes and helps narrow down the differentials. It is potentially a diagnostic test of choice in younger children. Our study helped to grade the severity of connective tissue disorders and establish the benignity of melanonychia. Photographic documentation facilitates record-keeping., Competing Interests: None., (Copyright: © 2023 Indian Journal of Dermatology.)

Competing Interests: There are no conflicts of interest.

Background: A sustained inflammatory state of psoriasis causes comorbidities such as psoriatic arthritis, metabolic syndrome (MetS), and cardiovascular disease., Aims: To note the duration and severity of psoriasis, assess prevalence of MetS, and correlate these with indicators of sub-clinical atherosclerosis., Methodology: Thirty-two patients of chronic plaque psoriasis were enrolled in the study. Their demographic particulars, clinical details, results of investigations to assess MetS, and indicators of sub-clinical atherosclerosis, namely, carotid intimal media thickness (CIMT) measured using B-mode USG and epicardial fat thickness (EFT) using 2-D ECHO, were recorded., Results: The study participants were predominantly male (2.5:1); their mean age was 40.45 ± 12.42 years, the median disease duration (DD) was 2 years, and the mean psoriatic area severity index (PASI) score was 8.62 ± 7.49. Mild disease (PASI <5) was present in 12 participants (37.5%), and shorter DD (<5 years) was present in 16 (50%) participants. MetS, detected in 11 (37.5%) study participants, was not significantly associated with CIMT, EFT, DD, and PASI. CIMT and EFT too did not correlate significantly with DD, PASI, or measures of MetS. Neither did there exist any significant correlation between CIMT and EFT., Conclusion: Sub-clinical atherosclerosis in our study participants was not significantly associated with either measures of MetS or duration/severity of psoriasis., Competing Interests: There are no conflicts of interest., (Copyright: © 2022 Indian Journal of Dermatology.)