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ObjectivesTo evaluate whether the performance of the modified Clinical Pulmonary Infection Score (CPIS) for diagnosis of ventilator-associated pneumonia (VAP) could be improved by incorporating procalcitonin (PCT), lactate and a standardised lung ultrasound score (LUS).DesignProspective observational study.SettingInternal medicine and respiratory intensive care units (ICUs) of a tertiary care centre in India over 18 months.Participants97 adult patients receiving invasive mechanical ventilation for 48 hours or more with suspected VAP.Primary and secondary outcome measuresThe primary outcome was the diagnostic accuracy (area under the receiver operating characteristic curve (AUC)) of the integrated score compared with a reference standard of quantitative bronchoalveolar lavage culture (>104 CFU/mL). Secondary outcomes included ICU length of stay and 14-day mortality.ResultsOf the 97 patients, 70 (72.2%) were culture positive. The modified CPIS alone demonstrated limited diagnostic utility (AUC 0.686). In contrast, the novel 4-point Lactate-Lung Ultrasound-Procalcitonin-Clinical Pulmonary Infection Score (LPCPIS) score (integrating CPIS >5, lactate >2.15 mmol/L, PCT >1.3 ng/mL and LUS >14) achieved excellent discriminatory ability (AUC 0.919, 95% CI 0.844 to 0.976). At a cut-off of ≥2, the score yielded 95.7% sensitivity and 77.8% specificity. Crucially, a 4-point LPCPIS bedside score of ≤1 was associated with a highly diminished probability of VAP, yielding a negative predictive value of 100% in this cohort. In comparison, a score ≥3 strongly indicated a high likelihood of VAP, with a positive predictive value of 96.5% and specificity of 92.6%, pending definitive microbiological confirmation.ConclusionsThe novel 4-point LPCPIS bedside score demonstrates significant diagnostic superiority over the traditional modified CPIS (AUC 0.919 vs 0.686). By integrating bedside lung ultrasound with biomarkers, this simplified pragmatic tool provides impressive discriminatory power, allowing clinicians in resource-limited settings to reliably assess probability of VAP and guide early antimicrobial decision-making while awaiting definitive microbiological cultures.

Graphitic carbon nitride (g-C 3 N 4 ) with well-controlled properties are the key for photocatalysis water splitting and organic pollutant degradation. A facile template-free synthesis method was utilized to optimize the polymerization temperature of melamine and urea for the preparation of highly active g-C 3 N 4 . The polymerization was carried out at different temperatures and ramping rate in tightly sealed crucible system with open air conditions. The crystalline and tri-s-triazine periodicity was significantly improved with increasing polymerization temperature. The results demonstrated that graphitic carbon nitride could not be fully formed below 550 °C. It was also found that the precursors and heating rate had a big influence on the photocatalytic activities. Higher photocatalytic activities were achieved by g-C 3 N 4 synthesized from urea at a lower heating rate.

The Mountain Ecosystems, such as the Himalayan region, not only provide numerous Ecosystem Services (ESs) to millions of people living in mountainous areas but also provide essential ES to those living downstream. However, there is often an uneven distribution of ES availability and its value across different regions. Therefore, a better understanding of the spatial heterogeneity of ES is required for efficient and sustainable management of ES. This study thoroughly reviews literary data to examine ES’s spatial distribution and economic values across the Western, Central, and Eastern Himalayas. The literature was searched in the Scopus online database using the Boolean method from specific keywords such as ‘Ecosystem Services’ AND ‘Himalayas’. A systematic review of 76 of the most relevant literature sources yielded 31 unique ES divided into three categories: Provisioning (PES), Regulating (RES), and Cultural (CES). The distribution of reviewed literature is relatively balanced across the Himalayas (Western: 30.26%, Central: 32.89%, Eastern: 36.84%); however, the analysis identified a bias towards PES (43.54%), highlighting a need for increased research focus on RES (36.48%) and CES (19.98%). Notably, water-related services such as PES9 (Surface water used as a material (non-drinking purposes)) and PES8 (Surface water for drinking) have been ranked highest in all regions of the Himalayas yet have not been comprehensively studied in terms of their quantification and valuation. Furthermore, while most literature focused on the identification of ES (73.68%), there is a significant lack of attention to quantification (39.47%) and valuation (23.68%) of ES in the region. The average economic values for PES, RES, and CES were 446.75 USD/ha/year, 1128.81 USD/ha/year, and 457.51 USD/ha/year, respectively, indicating higher valuation for RES. This underlines the need for a more balanced research approach that includes identifying and thoroughly quantifying and valuing all types of ES in the Himalayas.

Cotton, being a crucial cash crop globally, faces significant challenges due to multiple diseases that adversely affect its quality and yield. To identify such diseases is very important for the implementation of effective management strategies for sustainable agriculture. Image recognition plays an important role for the timely and accurate identification of diseases in cotton plants as it allows farmers to implement effective interventions and optimize resource allocation. Additionally, deep learning has begun as a powerful technique for to detect diseases in crops using images. Hence, the significance of this work lies in its potential to mitigate the impact of these diseases, which cause significant damage to the cotton and decrease fibre quality and promote sustainable agricultural practices. This paper investigates the role of deep transfer learning techniques such as EfficientNet models, Xception, ResNet models, Inception, VGG, DenseNet, MobileNet, and InceptionResNet for cotton plant disease detection. A complete dataset of infected cotton plants having diseases like Bacterial Blight, Target Spot, Powdery Mildew, Aphids, and Army Worm along with the healthy ones is used. After pre-processing the images of the dataset, their region of interest is obtained by applying feature extraction techniques such as the generation of the biggest contour, identification of extreme points, cropping of relevant regions, and segmenting the objects using adaptive thresholding. During experimentation, it is found that the EfficientNetB3 model outperforms in accuracy, loss, as well as root mean square error by obtaining 99.96%, 0.149, and 0.386 respectively. However, other models also show the good performance in terms of precision, recall, and F1 score, with high scores close to 0.98 or 1.00, except for VGG19. The findings of the paper emphasize the prospective of deep transfer learning as a viable technique for cotton plant disease diagnosis by providing a cost-effective and efficient solution for crop disease monitoring and management. This strategy can also help to improve agricultural practices by ensuring sustainable cotton farming and increased crop output.

Lung cancer is the most common neoplastic disorder associated with paraneoplastic syndromes. The most common paraneoplastic syndromes are the syndrome of inappropriate release of antidiuretic hormone (SIADH), hypercalcemia of malignancy, ectopic Cushing's syndrome, and various other neurological syndromes. A few case reports have reported gynecomastia as a paraneoplastic syndrome. Recognition of this uncommon presentation can aid in the early detection of associated malignancies, thus potentially improving outcomes. In this article, we are presenting the case of a male patient in his late sixties who, on presentation, had gynecomastia and was eventually diagnosed with non-small-cell lung cancer (NSCLC).

Chronic obstructive pulmory disease (COPD) is a major cause of morbidity and mortality throughout the world. The coexistence of COPD and obstructive sleep apnea (OSA) (i.e., overlap syndrome) has been reported in several studies. Both disorders independently increase the risk of cardiovascular complications. Hence, there is a theoretical possibility that cardiovascular parameters may be worse in patients with overlap syndrome compared to those with only COPD. However, this has been sparsely assessed in the literature. This study aimed to compare the clinical characteristics, echocardiography, and sleep parameters amongst COPD patients with and without sleep-related breathing disorders (SRBD). This observatiol, cross-sectiol study included 30 patients with stable COPD. All participants underwent a detailed clinical evaluation, followed by level 1 polysomnography (PSG). Each participant underwent echocardiographic evaluation to estimate mean pulmory artery pressure from right ventricular systolic pressure (RVSP). Based on their PSG findings, participants were classified into non-SRBD and SRBD groups. Both groups were further compared with respect to clinical characteristics, echocardiographic, and PSG parameters. We found that most of the participants (93.3%) were male, and the mean age of the study population was 56±8.2 years. The only SRBD identified in this study was OSA, which was observed in 80% of participants. In this group, OSA was not associated with obesity. Systemic hypertension (50%) was the most common comorbidity, followed by diabetes mellitus (26.67%), but neither was significantly different between the groups. The mean RVSP was significantly higher among OSA patients than non-OSA patients (41.25±14.98 vs. 30.83±5.84, respectively; p=0.01). OSA was seen in 80% of participants with stable COPD, even in the absence of obesity. The presence of OSA was associated with a higher RVSP in this patient group.

In recent years, physics-informed neural networks (PINN) have been used to solve stiff-PDEs mostly in the 1D and 2D spatial domain. PINNs still experience issues solving 3D problems, especially, problems with conflicting boundary conditions at adjacent edges and corners. These problems have discontinuous solutions at edges and corners that are difficult to learn for neural networks with a continuous activation function. In this review paper, we have investigated various PINN frameworks that are designed to solve stiff-PDEs. We took two heat conduction problems (2D and 3D) with a discontinuous solution at corners as test cases. We investigated these problems with a number of PINN frameworks, discussed and analysed the results against the FEM solution. It appears that PINNs provide a more general platform for parameterisation compared to conventional solvers. Thus, we have investigated the 2D heat conduction problem with parametric conductivity and geometry separately. We also discuss the challenges associated with PINNs and identify areas for further investigation.

Bronchoscopy is an extremely useful diagnostic and therapeutic procedure that finds an invaluable place in Pulmonology practice. Due to aerosol generation, bronchoscopy carries a high risk of infection transmission to health care workers and other patients. This fact is even more important in the present times of COVID-19 pandemic owing to its droplet- (and possibly aerosol-) mediated spread. With this background, a working group extracted literature through electronic search of PubMed and Google Scholar databases. All relevant documents were comprehensively reviewed and consensus recommendations formulated based on the level of available evidence. Where evidence was insufficient, Usual Practice Points were formulated based on expert opinion. This resultant document attempts to present clinical recommendations for performing flexible bronchoscopy in COVID-19 suspect/confirmed patients. It outlines important general considerations for bronchoscopy in these cases, provides an algorithmic approach to patient selection for bronchoscopy during these extraordinary times, and enlists critical do's and don'ts that should be followed before, during, and after the procedure. To conclude, flexible bronchoscopy must be cautiously performed amid the COVID-19 crisis. Judicious case selection and meticulous contact and airborne precautions are important to minimise infection transmission.

Internet of Things (IoT) is a rapidly expanding technology composed of devices embedded with sensors, specialized hardware, and software to connect with other devices and servers on the internet. With this rapid expansion, it faces some major hurdles in data secrecy, privacy, and device integrity, and perhaps, in general, digital identity has been a major problem on the internet. The World Wide Web Consortium (W3C) has provided a standardized format through which one can specify his identity called Decentralised Identifiers (DIDs). Blockchain technology, alongside decentralized identifiers and public-key cryptography, offers a peer-to-peer identity and credential management model through self-sovereign identity. Combining the decentralized identity and blockchains provides us with verifiable credentials through which one can verify the identities of transacting peers by decentralized registration and discovery of public keys needed to verify digital signatures. This combined framework is trusted, permissioned, supports device registration and verification along with revocation of device credentials, is secure, respects the privacy of the participating nodes, and maintains the integrity of their data. With several new communication protocols being developed for IoT and leveraging the Blockchain protocols in the domain of IoT, this paper quantitatively compares the performance of the well-known IoT communication protocols such as CoAP, MQTT, and DIDComm in real-time testbed on various parameters, and the results are tabulated. Device data has also been replicated across multiple peers using decentralized storage such as InterPlanetary File System (IPFS).

Bronchoscopy-related bleeding is often encountered and is usually self-limiting or controllable by conservative measures. However, major bleeds can be life threatening for the patient as well as challenging for the physician to manage. There are several methods to achieve adequate haemostasis should a significant airway bleed occur. In this context, we describe a patient who had a post-bronchoscopic debulking bleed which persisted despite use of all available measures, and we deployed a self-expanding metallic stent in an attempt to control it. To the best of our knowledge, this is the first instance of a metallic airway stent being used to control bronchoscopy associated bleeding, though reports of its usage in management of intractable haemoptysis exist in the literature.