Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements.

The development, application, and benchmarking of artificial intelligence (AI) tools to improve diagnosis, prognostication, and therapy in neuro-oncology are increasing at a rapid pace. This Policy Review provides an overview and critical assessment of the work to date in this field, focusing on diagnostic AI models of key genomic markers, predictive AI models of response before and after therapy, and differentiation of true disease progression from treatment-related changes, which is a considerable challenge based on current clinical care in neuro-oncology. Furthermore, promising future directions, including the use of AI for automated response assessment in neuro-oncology, are discussed.

Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice.

Technological advancements have enabled the extended investigation, development, and application of computational approaches in various domains, including health care. A burgeoning number of diagnostic, predictive, prognostic, and monitoring biomarkers are continuously being explored to improve clinical decision making in neuro-oncology. These advancements describe the increasing incorporation of artificial intelligence (AI) algorithms, including the use of radiomics. However, the broad applicability and clinical translation of AI are restricted by concerns about generalisability, reproducibility, scalability, and validation. This Policy Review intends to serve as the leading resource of recommendations for the standardisation and good clinical practice of AI approaches in health care, particularly in neuro-oncology. To this end, we investigate the repeatability, reproducibility, and stability of AI in response assessment in neuro-oncology in studies on factors affecting such computational approaches, and in publicly available open-source data and computational software tools facilitating these goals. The pathway for standardisation and validation of these approaches is discussed with the view of trustworthy AI enabling the next generation of clinical trials. We conclude with an outlook on the future of AI-enabled neuro-oncology.

Recent advancements in battery thermal management system (BTMS): A review of performance enhancement techniques with an emphasis on nano-enhanced phase change materials.

Because of their numerous benefits such as high charge cycle count, low self-discharge rate, low maintenance requirements, and tiny footprint, Li-batteries have been extensively employed in recent times. However, mostly Li-batteries have a limited lifespan of up to three years after production, may catch fire if the separator is damaged, and cannot be recharged when they are fully depleted. Due to the significant heat generation that li-batteries produce while they are operating, the temperature difference inside the battery module rises. This reduces the operating safety of battery and limits its life. Therefore, maintaining safe battery temperatures requires efficient thermal management using both active and passive. Thermal optimization may be achieved battery thermal management system (BTMS) that employs phase change materials (PCMs). However, PCM's shortcomings in secondary heat dissipation and restricted thermal conductivity still require development in the design, structure, and materials used in BTMS. We summarize new methods to control temperature of batteries using Nano-Enhanced Phase Change Materials (NEPCMs), air cooling, metallic fin intensification, and enhanced composite materials using nanoparticles which work well to boost their performance. To the scientific community, the idea of nano-enhancing PCMs is new and very appealing. Hybrid and ternary battery modules are already receiving attention for the li-battery life span enhancement ultimately facilitating their broader adoption across various applications, from portable electronics to electric vehicles and beyond.

Leveraging real-world data to predict cancer cachexia stage, quality of life, and survival in a racially and ethnically diverse multi-institutional cohort of treatment-naïve patients with pancreatic ductal adenocarcinoma.

Introduction: Cancer-associated cachexia (CC) is a progressive syndrome characterized by unintentional weight loss, muscle atrophy, fatigue, and poor outcomes that affects most patients with pancreatic ductal adenocarcinoma (PDAC). The ability to identify and classify CC stage along its continuum early in the disease process is challenging but critical for management. Objectives: The main objective of this study was to determine the prevalence of CC stage overall and by sex and race and ethnicity among treatment-naïve PDAC cases using clinical, nutritional, and functional criteria. Secondary objectives included identifying the prevalence and predictors of higher symptom burden, supportive care needs, and quality of life (QoL), and examining their influence on overall survival (OS). Materials and methods: A population-based multi-institutional prospective cohort study of patients with PDAC was conducted between 2018 and 2021 by the Florida Pancreas Collaborative. Leveraging patient-reported data and laboratory values, participants were classified at baseline into four stages [non-cachexia (NCa), pre-cachexia (PCa), cachexia (Ca), and refractory cachexia (RCa)]. Multivariate regression, Kaplan Meier analyses, and Cox regression were conducted to evaluate associations. Results: CC stage was estimated for 309 PDAC cases (156 females, 153 males). The overall prevalence of NCa, PCa, Ca, and RCa was 12.9%, 24.6%, 54.1%, and 8.4%, respectively. CC prevalence across all CC stages was highest for males and racial and ethnic minorities. Criteria differentiated NCa cases from other groups, but did not distinguish PCa from Ca. The most frequently reported symptoms included weight loss, fatigue, pain, anxiety, and depression, with pain significantly worsening over time. The greatest supportive care needs included emotional and physical domains. Males, Black people, and those with RCa had the worst OS. Conclusions: Using clinical, nutritional, and functional criteria, nearly one-quarter of the PDAC cases in our diverse, multi-institutional cohort had PCa and 62.5% had Ca or RCa at the time of diagnosis. The PCa estimate is higher than that reported in prior studies. We recommend these criteria be used to aid in CC classification, monitoring, and management of all incident PDAC cases. Findings also highlight the recommendation for continued emotional support, assistance in alleviating pain, and supportive care needs throughout the PDAC treatment journey.

Multimodal data integration for oncology in the era of deep neural networks: a review.

Cancer research encompasses data across various scales, modalities, and resolutions, from screening and diagnostic imaging to digitized histopathology slides to various types of molecular data and clinical records. The integration of these diverse data types for personalized cancer care and predictive modeling holds the promise of enhancing the accuracy and reliability of cancer screening, diagnosis, and treatment. Traditional analytical methods, which often focus on isolated or unimodal information, fall short of capturing the complex and heterogeneous nature of cancer data. The advent of deep neural networks has spurred the development of sophisticated multimodal data fusion techniques capable of extracting and synthesizing information from disparate sources. Among these, Graph Neural Networks (GNNs) and Transformers have emerged as powerful tools for multimodal learning, demonstrating significant success. This review presents the foundational principles of multimodal learning including oncology data modalities, taxonomy of multimodal learning, and fusion strategies. We delve into the recent advancements in GNNs and Transformers for the fusion of multimodal data in oncology, spotlighting key studies and their pivotal findings. We discuss the unique challenges of multimodal learning, such as data heterogeneity and integration complexities, alongside the opportunities it presents for a more nuanced and comprehensive understanding of cancer. Finally, we present some of the latest comprehensive multimodal pan-cancer data sources. By surveying the landscape of multimodal data integration in oncology, our goal is to underline the transformative potential of multimodal GNNs and Transformers. Through technological advancements and the methodological innovations presented in this review, we aim to chart a course for future research in this promising field. This review may be the first that highlights the current state of multimodal modeling applications in cancer using GNNs and transformers, presents comprehensive multimodal oncology data sources, and sets the stage for multimodal evolution, encouraging further exploration and development in personalized cancer care.

Metal(loid) uptake and physiological response of Coix lacryma-jobi L. to soil potentially toxic elements in a polluted metal-mining area.

Coix lacryma-jobi L. is a traditional medicinal plant in east Asia and is an important crop in Guizhou province, southwest China, where there are elevated levels of soil mercury and arsenic (As). Exposure to multiple potentially toxic elements (PTEs) may affect plant accumulation of metal(loid)s and food safety in regions with high geological metal concentrations. Field experiments were conducted to study the effects of PTEs on metal(loid) accumulation and physiological response of C. lacryma in different plant parts at three pollution levels. Total root length, number of root tips, number of branches, and number of root crosses increased with increasing pollution level, with increases in highly polluted areas of 44.2, 57.0, 79.6, and 97.2%, respectively, compared to lightly polluted areas. Under multi-element stress the activity of C. lacryma antioxidant oxidase showed an increase at low and medium PTE concentrations and inhibition at high concentrations. The As contents were all below the maximum limit of cereal food contaminants in China (GB 2762-2022, As < 0.5 mg kg-1). The stems had high Tl bioconcentration factors but the translocation factors from stem to grain were very low, indicating that the stems may be a key plant part restricting Tl transport to the grains. C. lacryma increased root retention and reduced the transport effect, thus reducing metal accumulation in the grains. C. lacryma adapted to PTE stress through root remodeling and enhanced antioxidant enzyme activities.

Fabrication of a salivary amylase electrochemical sensor based on surface confined MWCNTs/ß-cyclodextrin/starch architect for dental caries in clinical samples.

Salivary α-amylase (α-ALS) has drawn attention as a possible bioindicator for dental caries. Herein, combining the synergistic properties of multi-walled carbon nanotubes (MWCNTs), ß-cyclodextrin (ß-CD) and starch, an electrochemical sensor is constructed employing ferrocene (FCN) as an electrochemical indicator to oversee the progression of the enzymatic catalysis of α-ALS. The method involves a two-step chemical reaction sequence on a screen-printed carbon electrode (SPCE). X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, Field emission scanning electron microscope (FE-SEM), and Dynamic light scattering (DLS) were used to characterize the synthesized material, while Static water Contact angle measurements, cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS) were performed to monitor each step of sensor fabrication. The electrochemical sensor permitted to detect α-ALS within the linear range of 0.5-280 U mL-1, revealing detection (LOD), and quantification (LOQ) values of 0.041 U mL-1, and 0.159 U mL-1, respectively. Remarkably, the sensor demonstrated exceptional specificity and selectivity, effectively discriminating against other interfering substances in saliva. Validation of the method involved analyzing α-ALS levels in artificial saliva with an accuracy range of 97 % to 103 %, as well as in real clinical saliva samples across various age groups.

Defining and Addressing Research Priorities in Cancer Cachexia through Transdisciplinary Collaboration.

For many patients, the cancer continuum includes a syndrome known as cancer-associated cachexia (CAC), which encompasses the unintended loss of body weight and muscle mass, and is often associated with fat loss, decreased appetite, lower tolerance and poorer response to treatment, poor quality of life, and reduced survival. Unfortunately, there are no effective therapeutic interventions to completely reverse cancer cachexia and no FDA-approved pharmacologic agents; hence, new approaches are urgently needed. In May of 2022, researchers and clinicians from Moffitt Cancer Center held an inaugural retreat on CAC that aimed to review the state of the science, identify knowledge gaps and research priorities, and foster transdisciplinary collaborative research projects. This review summarizes research priorities that emerged from the retreat, examples of ongoing collaborations, and opportunities to move science forward. The highest priorities identified include the need to (1) evaluate patient-reported outcome (PRO) measures obtained in clinical practice and assess their use in improving CAC-related outcomes; (2) identify biomarkers (imaging, molecular, and/or behavioral) and novel analytic approaches to accurately predict the early onset of CAC and its progression; and (3) develop and test interventions (pharmacologic, nutritional, exercise-based, and through mathematical modeling) to prevent CAC progression and improve associated symptoms and outcomes.

Pancreatic Ductal Adenocarcinoma (PDAC): A Review of Recent Advancements Enabled by Artificial Intelligence.

Pancreatic Ductal Adenocarcinoma (PDAC) remains one of the most formidable challenges in oncology, characterized by its late detection and poor prognosis. Artificial intelligence (AI) and machine learning (ML) are emerging as pivotal tools in revolutionizing PDAC care across various dimensions. Consequently, many studies have focused on using AI to improve the standard of PDAC care. This review article attempts to consolidate the literature from the past five years to identify high-impact, novel, and meaningful studies focusing on their transformative potential in PDAC management. Our analysis spans a broad spectrum of applications, including but not limited to patient risk stratification, early detection, and prediction of treatment outcomes, thereby highlighting AI's potential role in enhancing the quality and precision of PDAC care. By categorizing the literature into discrete sections reflective of a patient's journey from screening and diagnosis through treatment and survivorship, this review offers a comprehensive examination of AI-driven methodologies in addressing the multifaceted challenges of PDAC. Each study is summarized by explaining the dataset, ML model, evaluation metrics, and impact the study has on improving PDAC-related outcomes. We also discuss prevailing obstacles and limitations inherent in the application of AI within the PDAC context, offering insightful perspectives on potential future directions and innovations.

Statistical analysis plan for the replacing protein via enteral nutrition in a stepwise approach in critically ill patients (REPLENISH) randomized clinical trial.

BACKGROUND: The optimal amount and timing of protein intake in critically ill patients are unknown. REPLENISH (Replacing Protein via Enteral Nutrition in a Stepwise Approach in Critically Ill Patients) trial evaluates whether supplemental enteral protein added to standard enteral nutrition to achieve a high amount of enteral protein given from ICU day five until ICU discharge or ICU day 90 as compared to no supplemental enteral protein to achieve a moderate amount of enteral protein would reduce all-cause 90-day mortality in adult critically ill mechanically ventilated patients. METHODS: In this multicenter randomized trial, critically ill patients will be randomized to receive supplemental enteral protein (1.2 g/kg/day) added to standard enteral nutrition to achieve a high amount of enteral protein (range of 2-2.4 g/kg/day) or no supplemental enteral protein to achieve a moderate amount of enteral protein (0.8-1.2 g/kg/day). The primary outcome is 90-day all-cause mortality; other outcomes include functional and health-related quality-of-life assessments at 90 days. The study sample size of 2502 patients will have 80% power to detect a 5% absolute risk reduction in 90-day mortality from 30 to 25%. Consistent with international guidelines, this statistical analysis plan specifies the methods for evaluating primary and secondary outcomes and subgroups. Applying this statistical analysis plan to the REPLENISH trial will facilitate unbiased analyses of clinical data. CONCLUSION: Ethics approval was obtained from the institutional review board, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia (RC19/414/R). Approvals were also obtained from the institutional review boards of each participating institution. Our findings will be disseminated in an international peer-reviewed journal and presented at relevant conferences and meetings. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04475666 . Registered on July 17, 2020.

Building Flexible, Scalable, and Machine Learning-Ready Multimodal Oncology Datasets.

The advancements in data acquisition, storage, and processing techniques have resulted in the rapid growth of heterogeneous medical data. Integrating radiological scans, histopathology images, and molecular information with clinical data is essential for developing a holistic understanding of the disease and optimizing treatment. The need for integrating data from multiple sources is further pronounced in complex diseases such as cancer for enabling precision medicine and personalized treatments. This work proposes Multimodal Integration of Oncology Data System (MINDS)-a flexible, scalable, and cost-effective metadata framework for efficiently fusing disparate data from public sources such as the Cancer Research Data Commons (CRDC) into an interconnected, patient-centric framework. MINDS consolidates over 41,000 cases from across repositories while achieving a high compression ratio relative to the 3.78 PB source data size. It offers sub-5-s query response times for interactive exploration. MINDS offers an interface for exploring relationships across data types and building cohorts for developing large-scale multimodal machine learning models. By harmonizing multimodal data, MINDS aims to potentially empower researchers with greater analytical ability to uncover diagnostic and prognostic insights and enable evidence-based personalized care. MINDS tracks granular end-to-end data provenance, ensuring reproducibility and transparency. The cloud-native architecture of MINDS can handle exponential data growth in a secure, cost-optimized manner while ensuring substantial storage optimization, replication avoidance, and dynamic access capabilities. Auto-scaling, access controls, and other mechanisms guarantee pipelines' scalability and security. MINDS overcomes the limitations of existing biomedical data silos via an interoperable metadata-driven approach that represents a pivotal step toward the future of oncology data integration.

Advances in Nanoparticle-Enhanced Thermoelectric Materials from Synthesis to Energy Harvesting: A Review.

This comprehensive review analysis examines the domain of composite thermoelectric materials that integrate nanoparticles, providing a critical assessment of their methods for improving thermoelectric properties and the procedures used for their fabrication. This study examines several approaches to enhance power factor and lattice thermal conductivity, emphasizing the influence of secondary phases and structural alterations. This study investigates the impact of synthesis methods on the electrical characteristics of materials, with a particular focus on novel techniques such as electrodeposition onto carbon nanotubes. The acquired insights provide useful guidance for the creation of new thermoelectric materials. The review also compares and contrasts organic and inorganic thermoelectric materials, with a particular focus on the potential of inorganic materials in the context of waste heat recovery and power production within industries. This analysis highlights the role of inorganic materials in improving energy efficiency and promoting environmental sustainability.

Host-Guest Mechanism via Induced Fit Fullerene Complexation in Porphin Receptor to Probe Salivary Alpha-Amylase in Dental Caries for Clinical Applications.

Salivary α-amylase is the most abundant protein of human saliva that potentially binds to streptococcus and other bacteria via specific surface-exposed α-amylase-binding proteins and plays a significant role in caries development. The detection of α-amylase in saliva can be used as a bioindicator of caries development. Herein, a facile strategy has been applied, tailoring the photochemical properties of 5,10,15,20-tetrakis(4-hydroxyphenyl)-21H,23H-porphine (TPPOH) and the fullerene C60 complex. The fluorescence emission of TPPOH is quenched by starch-coated fullerene C60 via charge-transfer effects, as determined by UV absorption and fluorescence spectroscopic studies. The starch-coated C60 has been thoroughly characterized via Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), optical microscopy, thermal gravimetric analysis (TGA), static water contact angle measurements, and zeta potential measurements. The analytical response of the assay showed a linear fluorescent response in α-amylase concentrations ranging from 0.001-0.1 Units/mL, with an LOD of 0.001 Units/mL. The applicability of the method was tested using artificial saliva with quantitative recoveries in the range 95-100%. The practicability of the procedure was verified by inspecting saliva samples of real clinical samples covering all age groups. We believe that the proposed method can serve as an alternative analytical method for caries detection and risk assessment that would also minimize the cost of professional preventive measures and treatments.

Unlocking the anti-diabetic potential of Gymnema sylvestre, Trigonella foenum-graecum, and their combination thereof: An in-vivo evaluation.

The current study aimed to explore the anti-diabetic effect of aqueous extracts of Gymnema sylvestre, Trigonella foenum-graecum and mixture of both the plants in alloxan-induced diabetic rabbits. A total of 30 rabbits were grouped into six equal groups as: normal control, diabetic control, diabetic treated with 300 mg/kg body weight (bw) G. sylvestre, diabetic treated with 300 mg/kg bw T. foenum-graecum, diabetic treated with 300 mg/kg bw mixture of both the plants and diabetic treated with 500 mg/kg bw metformin for 4 weeks. Diabetes was induced to all the study group animals except normal control by intravenous administration of alloxan monohydrate (80 mg/kg bw). Blood glucose was measured by glucometer and other biochemical parameters were determined through various kit methods. Serum insulin was measured through ELISA kit method. Results showed that both the plants and metformin significantly (p < .05) decreased the fasting blood glucose. Hypoglycemic activity of aqueous extract of G. sylvestre and metformin was found slightly higher than aqueous extract of T. foenum-graecum and the mixture of both the plants. However, a significant (p < .05) rise in insulin secretion was observed in studied plants extract treated rabbits. Serum urea, creatinine, and liver enzymes were found reduced significantly (p < .05) in treated rabbits whereas packed cell volume was also returned to normal in treated animals as compared to control group. The study concluded that G. sylvestre and T. foenum-graecum extracts have comparable effects with metformin in normalizing the blood glucose level and have more pronounced effect than metformin in restoring the serum biochemical parameters to normal levels. Hence, these plants may be the good alternative medicine in managing the diabetes mellitus.

Dual solutions of unsteady flow of copper-alumina/water based hybrid nanofluid with acute magnetic force and slip condition.

Suspending particles of tiny solid in a fluid used to transport energy can enhance its thermal conductivity and heat transport properties. Our main goal of this examination is to study the radiative unsteady two-dimensional (2D) flow on a continuously diminishing, horizontal sheet. with suction for the hybrid water-based nanofluid and an aligned field of magnetic, including the combined suction, magnetic, and velocity slip conditions effect. The Tiwari & Das model of nanofluid equations is used, which takes into consideration the solid volume percentage. Equations of similarity are derived by employing the transformations of similarity, and the associated equations have been simplified numerically by employing the bvp4c method in MATLAB software for a variety of values of the nanoparticle volume fraction, the unsteadiness, and the wall mass suction in water. It is discovered that, within the given the unsteadiness parameter range, two solutions exist. Moreover, it is found that the fluid velocity slows down in 1st solution as volume fraction of copper nanoparticles rises but speeds up in the second solution at first before slowing down again. Using a temporal stability analysis, it is found that only one of the dual branches is stable over the long run, while the other is unstable.

Stigma towards mental illness in Asian nations and low-and-middle-income countries, and comparison with high-income countries: A literature review and practice implications.

Background: Stigma related to mental illness (and its treatment) is prevalent worldwide. This stigma could be at the structural or organizational level, societal level (interpersonal stigma), and the individual level (internalized stigma). Vulnerable populations, for example, gender minorities, children, adolescents, and geriatric populations, are more prone to stigma. The magnitude of stigma and its negative influence is determined by socio-cultural factors and macro (mental health policies, programs) or micro-level factors (societal views, health sectors, or individuals' attitudes towards mentally ill persons). Mental health stigma is associated with more serious psychological problems among the victims, reduced access to mental health care, poor adherence to treatment, and unfavorable outcomes. Although various nationwide and well-established anti-stigma interventions/campaigns exist in high-income countries (HICs) with favorable outcomes, a comprehensive synthesis of literature from the Low- and Middle-Income Countries (LMICs), more so from the Asian continent is lacking. The lack of such literature impedes growth in stigma-related research, including developing anti-stigma interventions. Aim: To synthesize the available mental health stigma literature from Asia and LMICs and compare them on the mental health stigma, anti-stigma interventions, and the effectiveness of such interventions from HICs. Materials and Methods: PubMed and Google Scholar databases were screened using the following search terms: stigma, prejudice, discrimination, stereotype, perceived stigma, associate stigma (for Stigma), mental health, mental illness, mental disorder psychiatric* (for mental health), and low-and-middle-income countries, LMICs, High-income countries, and Asia, South Asian Association for Regional Cooperation/SAARC (for countries of interest). Bibliographic and grey literature were also performed to obtain the relevant records. Results: The anti-stigma interventions in Asia nations and LMICs are generalized (vs. disorder specific), population-based (vs. specific groups, such as patients, caregivers, and health professionals), mostly educative (vs. contact-based or attitude and behavioral-based programs), and lacking in long-term effectiveness data. Government, international/national bodies, professional organizations, and mental health professionals can play a crucial in addressing mental health stigma. Conclusion: There is a need for a multi-modal intervention and multi-sectoral coordination to mitigate the mental health stigma. Greater research (nationwide surveys, cultural determinants of stigma, culture-specific anti-stigma interventions) in this area is required.

Molecular characterization of mitochondrial COI gene sequences in Micraspis allardi from Pakistan.

The Coccinellidae is a highly diversified family of order Coleoptera. Coccinellid ladybirds are well known for their role as biological control agent against varied range of agricultural pests. The samples of coccinellid ladybird collected from Pakistan were identified and characterized as Micraspis allardi (Mulsant, 1866). This is one of the least-studied ladybird species with limited work on its ecological distribution as a biological control agent. The genus Micraspis has vast genetic diversity with a possible presence of unknown number of cryptic species. Sequence information of some species of the genus Micraspis are present in NCBI database. However, least molecular data or sequences describing M. allardi could be available from database. Therefore, morphological and molecular characterization was imperative for this species. Here, the samples collected from sugarcane field of Faisalabad District of Pakistan and were identified by using morphological and molecular protocols. For molecular identification, two different regions of mitochondrial cytochrome c oxidase I (COI) gene (COI-5' and COI- 3') were used as molecular markers for the identification of the species. Morphological appearance, DNA sequence similarity searches and phylogenetic analysis collectively indicated it as M. allardi. To the best of our knowledge, this is the first report providing molecular evidence of M. allardi using mitochondrial DNA barcode region (658bp) as well as mtCOI-3' sequences (817bp). The study will help in understanding population genetics through diversity analysis, ecological role, and phenotypic structures associated with the geographic range of this species.

Mineral Solubilizing Rhizobacterial Strains Mediated Biostimulation of Rhodes Grass Seedlings.

Minerals play a dynamic role in plant growth and development. However, most of these mineral nutrients are unavailable to plants due to their presence in fixed forms, which causes significant losses in crop production. An effective strategy to overcome this challenge is using mineral solubilizing bacteria, which can convert insoluble forms of minerals into soluble ones that plants can quickly assimilate, thus enhancing their availability in nutrient-depleted soils. The main objective of the present study was to isolate and characterize mineral solubilizing rhizobacteria and to assess their plant growth-promoting potential for Rhodes grass. Twenty-five rhizobacterial strains were isolated on a nutrient agar medium. They were characterized for solubilization of insoluble minerals (phosphate, potassium, zinc, and manganese), indole acetic acid production, enzymatic activities, and various morphological traits. The selected strains were also evaluated for their potential to promote the growth of Rhodes grass seedlings. Among tested strains, eight strains demonstrated strong qualitative and quantitative solubilization of insoluble phosphate. Strain MS2 reported the highest phosphate solubilization index, phosphate solubilization efficiency, available phosphorus concentration, and reduction in medium pH. Among tested strains, 75% were positive for zinc and manganese solubilization, and 37.5% were positive for potassium solubilization. Strain MS2 demonstrated the highest quantitative manganese solubilization, while strains MS7 and SM4 reported the highest solubilization of zinc and potassium through acidifying their respective media. The strain SM4 demonstrated the most increased IAA production in the presence and absence of L-tryptophan. The majority of strains were positive for various enzymes, including urease, catalase protease, and amylase activities. However, these strains were negative for coagulase activity except strains SM7 and MS7. Based on 16S rRNA gene sequencing, six strains, namely, SM2, SM4, SM5, MS1, MS2, and MS4, were identified as Bacillus cereus, while strains SM7 and MS7 were identified as Staphylococcus saprophyticus and Staphylococcus haemolyticus. These strains significantly improved growth attributes of Rhodes grass, such as root length, shoot length, and root and shoot fresh and dry biomasses compared to the uninoculated control group. The present study highlights the significance of mineral solubilizing and enzyme-producing rhizobacterial strains as potential bioinoculants to enhance Rhodes grass growth under mineral-deficient conditions sustainably.

Revolutionizing Digital Pathology With the Power of Generative Artificial Intelligence and Foundation Models.

Digital pathology has transformed the traditional pathology practice of analyzing tissue under a microscope into a computer vision workflow. Whole-slide imaging allows pathologists to view and analyze microscopic images on a computer monitor, enabling computational pathology. By leveraging artificial intelligence (AI) and machine learning (ML), computational pathology has emerged as a promising field in recent years. Recently, task-specific AI/ML (eg, convolutional neural networks) has risen to the forefront, achieving above-human performance in many image-processing and computer vision tasks. The performance of task-specific AI/ML models depends on the availability of many annotated training datasets, which presents a rate-limiting factor for AI/ML development in pathology. Task-specific AI/ML models cannot benefit from multimodal data and lack generalization, eg, the AI models often struggle to generalize to new datasets or unseen variations in image acquisition, staining techniques, or tissue types. The 2020s are witnessing the rise of foundation models and generative AI. A foundation model is a large AI model trained using sizable data, which is later adapted (or fine-tuned) to perform different tasks using a modest amount of task-specific annotated data. These AI models provide in-context learning, can self-correct mistakes, and promptly adjust to user feedback. In this review, we provide a brief overview of recent advances in computational pathology enabled by task-specific AI, their challenges and limitations, and then introduce various foundation models. We propose to create a pathology-specific generative AI based on multimodal foundation models and present its potentially transformative role in digital pathology. We describe different use cases, delineating how it could serve as an expert companion of pathologists and help them efficiently and objectively perform routine laboratory tasks, including quantifying image analysis, generating pathology reports, diagnosis, and prognosis. We also outline the potential role that foundation models and generative AI can play in standardizing the pathology laboratory workflow, education, and training.

Assessing spatiotemporal trends of total and extreme precipitation in a subtropical highland region: A climate perspective.

This study used a dataset of 30 years (1990-2020) of daily observations from 24 meteorological stations in the northern highlands of Pakistan to assess trends in extreme precipitation indices. The RClimDex model was used to analyze the indices, and the Modified Mann-Kendal test and the Theil-Sen slope estimator were applied to determine trends and slopes, respectively. The results showed a significant decrease in total annual precipitation amount (PRCPTOT) with varying rates of negative trend from -4.44 mm/year to -19.63 mm/year. The total winter and monsoon precipitation amounts were also decreased during the past three decades. The intensity-based precipitation indices (RX1Day, RX5Day, R95p, R99p, and SDII) showed a significant decrease in extreme intensity events over time, while the count of consecutive dry days (CDD) and consecutive wet days (CWD) indicated a significant decrease in duration at multiple stations. The annual counts of days with precipitation more than or equal to 10 mm (R10), 20 mm (R20), and 25 mm (R25) exhibited a significant decrease in frequency of extreme precipitation events, with the decrease more pronounced in the northern parts of the study domain. The findings of this study indicate a significant decline in the intensity, frequency, and extent of precipitation extremes across the northern highlands of Pakistan over the past 30 years.