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A rare histological variant of transitional cell urothelial carcinoma, the plasmacytoid variant, was recently included in the World Health Organization classification of urothelial tract tumors. This variant has a morphological resemblance to other tumors, which poses a diagnostic challenge for identifying this tumor and may often lead to misdiagnosis. Vigilant histopathological analysis and immunostaining are required to delineate the correct diagnosis. The plasmacytoid variant of urothelial carcinoma is an aggressive tumor with a poor prognosis, making correct diagnosis essential for early and appropriate treatment. This paper presents the case of a 46-year-old male with a plasmacytoid variant of high-grade urothelial carcinoma who underwent transurethral resection of a bladder tumor, received chemotherapy, and is currently undergoing follow-up.

Ovarian granulosa cell tumors (GCTs) are rare neoplasms with a unique incidence pattern peaking in postmenopausal women. This case report presents two instances of stage 4 recurrent adult GCTs with a prolonged 20-year follow-up. Patient 1, diagnosed at 54 years, experienced multiple recurrences managed through surgery, hormonal therapy, and chemotherapy, culminating in hepatocellular carcinoma. Patient 2, diagnosed at 67 years, underwent various treatments, including surgery, chemotherapy, and hormonal therapy, demonstrating disease stability. Despite the generally favorable prognosis, these cases highlight the challenges of managing recurrent GCTs, emphasizing the need for tailored therapeutic approaches.

Background  Metastatic castration-sensitive prostate cancer is defined as prostate cancer with de novo metastatic disease that responds to androgen deprivation therapy by keeping the testosterone levels low. Endogenous androgen synthesis is further blocked by abiraterone acetate along with prednisolone and indicated in patients with metastatic castration-sensitive prostate cancer. However, over time, these patients will become castration-resistant. The time from castration-sensitive to castration-resistant in our population is short, which calls for further investigation on a larger scale to explore factors such as genetics and environmental influences that may play a significant role.  Methodology  This retrospective study involved 47 adult patients aged 40 years and older. It focused exclusively on patients who were presented with de novo metastatic castration-sensitive disease and were treated with upfront abiraterone acetate. The study was carried out at the Department of Medical Oncology, Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan. Patient data spanning 10 years, from 2014 to 2024, was collected from hospital records. Results  The cohort demonstrated a median progression-free survival (PFS) of 20.7 months and a median overall survival (OS) of 38.4 months. These outcomes represent the entire study population, irrespective of subgroup classification. Different subgroup analyses do not show any statistically significant difference.  Conclusion  In our study, OS and PFS were lower than those reported in landmark studies conducted on similar populations in Western countries. This disparity highlights the need for further research in subcontinental populations to investigate potential contributing factors, including environmental influences or genetic variations.

Introduction This study aimed to evaluate treatment outcomes of human epidermal growth factor receptor 2 (HER2)-directed therapies in patients with non-metastatic HER2-positive breast cancer treated in a low-resource setting. Specifically, we assessed the impact of dual blockade (trastuzumab and pertuzumab), trastuzumab alone, or no HER2-targeted therapy on rates of residual disease, pathological complete response (pCR), progression-free survival (PFS), and overall survival (OS). Methods We conducted a retrospective cohort study at Shaukat Khanum Memorial Cancer Hospital, including 299 patients with non-metastatic HER2-positive breast cancer treated with neoadjuvant chemotherapy and either dual HER2 blockade, trastuzumab alone, or no HER2-targeted therapy due to financial constraints. Patient demographics, clinical features, treatments, and outcomes were analyzed using descriptive statistics, chi-square tests, and Kaplan-Meier survival analysis. Results The median age at diagnosis was 45.7 years (standard deviation±8.9). A majority of patients were premenopausal (n=222; 74.2%), and the majority presented with a palpable lump (n=275; 91.9%). Tumors were mainly located in the left (n=149; 49.8%) or right breast (n=147; 49.2%), with bilateral involvement in 3 (1.0%) cases. Invasive ductal carcinoma was the predominant histology (n=275; 91.9%), with estrogen receptor and progesterone receptor positivity observed in 185 (61.9%) and 179 (59.9%) patients, respectively. Grade III tumors were observed in 156 (52.2%) cases, and most tumors were T2 stage (n=236; 78.9%) with axillary nodal involvement in 232 (77.6%). Patients receiving dual HER2 blockade achieved a pCR in 45 (54.9%) of 82 cases, compared to 51 (45.9%) of 111 with trastuzumab alone, and 39 (36.8%) of 106 with no HER2 therapy (p=0.046). The docetaxel, carboplatin, trastuzumab, and pertuzumab (TCHP) regimen had the highest pCR rate in 19 (65.5%) of 29 patients (p<0.001). Grade III tumors were associated with higher pCR than Grade II (n=96; 56.5% vs. n=39; 30.2%; p<0.001). At 60 months, PFS was 236 (79.0%) overall, highest in the dual blockade group (n=73; 89.0%), followed by trastuzumab (n=96; 86.5%) and no HER2 therapy (n=69; 65.1%). OS at 60 months was 271 (90.6%), highest in the dual blockade group (n=78; 95.1%), then trastuzumab (n=102; 91.9%) and no HER2 therapy (n=79; 74.5%). Achieving pCR was associated with improved PFS and OS. Differences in both outcomes across groups were statistically significant (p<0.001). Conclusion Dual HER2 blockade significantly improved pCR, PFS, and OS in non-metastatic HER2-positive breast cancer. These findings support the inclusion of HER2-targeted agents in standard neoadjuvant treatment, even in resource-limited settings. Addressing barriers to access remains essential to improving global outcomes in breast cancer care.

Climate change may cause severe hydrological droughts, leading to water shortages which will require to be assessed using high-resolution data. Gravity Recovery and Climate Experiment (GRACE) satellite Terrestrial Water Storage (TWSA) estimates offer a promising solution to monitor hydrological drought, but its coarse resolution (1°) limits its applications to small regions of the Indus Basin Irrigation System (IBIS). Here we employed machine learning models such as Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) to downscale GRACE TWSA from 1° to 0.25°. The findings revealed that the XGBoost model outperformed the ANN model with Nash Sutcliff Efficiency (NSE) (0.99), Pearson correlation (R) (0.99), Root Mean Square Error (RMSE) (5.22 mm), and Mean Absolute Error (MAE) (2.75 mm) between the predicted and GRACE-derived TWSA. Further, Water Storage Deficit Index (WSDI) and WSD (Water Storage Deficit) were used to determine the severity and episodes of droughts, respectively. The results of WSDI exhibited a strong agreement when compared with the Standardized Precipitation Evapotranspiration Index (SPEI) at different time scales (1-, 3-, and 6-months) and self-calibrated Palmer Drought Severity Index (sc-PDSI). Moreover, the IBIS had experienced increasing drought episodes, e.g., eight drought episodes were detected within the years 2010 and 2016 with WSDI of −1.20 and −1.28 and total WSD of −496.99 mm and −734.01 mm, respectively. The Partial Least Square Regression (PLSR) model between WSDI and climatic variables indicated that potential evaporation had the largest influence on drought after precipitation. The findings of this study will be helpful for drought-related decision-making in IBIS.

Modern data analytics techniques and computationally inexpensive software tools are fueling the commercial applications of data-driven decision making and process optimization strategies for complex industrial operations. In this paper, modern and reliable process modeling techniques, i.e., multiple linear regression (MLR), artificial neural network (ANN), and least square support vector machine (LSSVM), are employed and comprehensively compared as reliable and robust process models for the generator power of a 660 MWe supercritical coal combustion power plant. Based on the external validation test conducted by the unseen operation data, LSSVM has outperformed the MLR and ANN models to predict the power plant’s generator power. Later, the LSSVM model is used for the failure mode recovery and a very successful operation control excellence tool. Moreover, by adjusting the thermo-electric operating parameters, the generator power on an average is increased by 1.74%, 1.80%, and 1.0 at 50% generation capacity, 75% generation capacity, and 100% generation capacity of the power plant, respectively. The process modeling based on process data and data-driven process optimization strategy building for improved process control is an actual realization of industry 4.0 in the industrial applications.

Satellite remote sensing is widely being used by the researchers and geospatial scientists due to its free data access for land observation and agricultural activities monitoring. The world is suffering from food shortages due to the dramatic increase in population and climate change. Various crop genotypes can survive in harsh climatic conditions and give more production with less disease infection. Remote sensing can play an essential role in crop genotype identification using computer vision. In many studies, different objects, crops, and land cover classification is done successfully, while crop genotypes classification is still a gray area. Despite the importance of genotype identification for production planning, a significant method has yet to be developed to detect the genotypes varieties of crop yield using multispectral radiometer data. In this study, three genotypes of wheat crop (Aas-‘2011’, ‘Miraj-‘08’, and ‘Punjnad-1) fields are prepared for the investigation of multispectral radio meter band properties. Temporal data (every 15 days from the height of 10 feet covering 5 feet in the circle in one scan) is collected using an efficient multispectral Radio Meter (MSR5 five bands). Two hundred yield samples of each wheat genotype are acquired and manually labeled accordingly for the training of supervised machine learning models. To find the strength of features (five bands), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Nonlinear Discernment Analysis (NDA) are performed besides the machine learning models of the Extra Tree Classifier (ETC), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), k Nearest Neighbor (KNN) and Artificial Neural Network (ANN) with detailed of configuration settings. ANN and random forest algorithm have achieved approximately maximum accuracy of 97% and 96% on the test dataset. It is recommended that digital policymakers from the agriculture department can use ANN and RF to identify the different genotypes at farmer's fields and research centers. These findings can be used for precision identification and management of the crop specific genotypes for optimized resource use efficiency.

The purpose of this study was to investigate the taxonomic diversity, richness, and distribution patterns of Poaceae in relation to abiotic factors in the Jhelum district of the Pakistan Himalayas. We used a random sampling technique from 80 grids within 240 sites with a rich diversity of wild grasses and 720 quadrates in triplets from each site across the Jhelum district between 2019 and 2021 to collect data on grass species and the associated environmental factors and conditions. After evaluating the important value index for each plant taxa and for the environmental data, we analyzed the data using ordination and cluster analysis techniques. Fifty-two Poaceae taxa from twenty-nine genera were recorded within the study area. From a total of 52 recorded Poaceae species, 45 were native and 7 were invasive species. The life form (biological) showed the dominancy of 27 therophyte species, followed by 24 hemicryptophyte species, and 1 geophyte species. Microphyll had the leading leaf size spectra (27 species), followed by nanophyll (12 species), macrophyll (10 species), and leptophyll (3 species). The trend of the life cycle was the maximum (27 spp.) during the monsoon season, followed by spring (11 spp.), winter (8 spp.), and summer (6 spp.). The leading genera were Setaria with 9.61% of the species, followed by Panicum, Cenchrus, and Brachiaria with 7.69% of the species. Aristida and Echinochloa made up 5.76% of the species while Chrysopogon, Digitaria, Eragrostis, Pennisetum, and Poa made up 3.84% of the species. Other genera recorded single species. The leaf size spectra of grasses were dominated by microphylls (50%) followed by nanophylls (23.07%), macrophylls (19.23%), and leptophylls (7.69%). On the basis of the importance value index, the most dominant species was Cynodon dactylon (68), followed by Dichanthium annulatum (58), Brachiaria ramose (38), Dactyloctenium aegyptium (37), Eleusine indica (35), Saccharum bengalense (33), and Cenchrus biflorus (28). Two-way cluster analyses classified the grasses into three plant community associations based on the indicator plant species. Soil parameters as subsamples were tested for moisture, pH, EC, OM, macronutrients (CaCO3, N, P, and K), and saturation while the ordination analysis revealed that they had a significant (p ≤ 0.002) effect on vegetation associations. Overall, this study contributes to a better understanding of the influence of environmental factors on the composition and associations of grass species and the development of scientifically informed management solutions for the ecological restoration of degraded habitats in this Himalayan region.

The escalating trend of global urbanization, particularly pronounced in the burgeoning urban areas of Pakistan, necessitates a meticulous examination of Land Use/Land Cover (LULC) changes and their extensive environmental repercussions. Understanding these transformations is crucial for informed decision-making in evolving ecological dynamics. This research utilizes Geographic Information Systems (GIS), Remote Sensing (RS), and statistical analyses to investigate LULC thoroughly changes in Okara District, Pakistan, from 1991 to 2023. Despite its considerable socio-economic significance, Okara District has remained relatively understudied, making this research a crucial contribution to understanding its evolving landscape. Key indicators include the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI), correlated with Land Surface Temperature (LST). Findings include a 22.6% decline in vegetation cover (415.1 km 2 ) and a 64.1% increase in urban areas (110.7 km 2 ) from 1991 to 2023. Correlations reveal a consistent negative relationship between NDVI and LST (R 2 , 0.55–0.69) and a positive correlation between LST and NDBI (R 2 0.62–0.69), indicating the persistent Urban Heat Island (UHI) effect. The study underscores the urgent need for sustainable urban planning to balance developmental needs and ecological preservation. Informed decision-making can mitigate the UHI effect, emphasizing the broader implications for global urbanization challenges. This research contributes to understanding LULC dynamics and fosters discussions on Sustainable Development Goals (SDGs), climate action, and the development of resilient cities and communities.

This research compares the use of the SAR (Sentinel-1) and Optical (Sentinel-2) sensors in identifying and mapping burnt and unburnt scars are rising during a bushfire in southeastern Australia and Margalla Hills, Islamabad, Pakistan, in 2019 and 2020. In order to evaluate the backscatter strength along with the Polarimetric decomposition portion, the C-band dual-polarized Sentinel-1 data was investigated to determine the magnitude of the burnt areas of forest cover in the study area. We could derive texture measurements from locally-based statistics using the Grey Level Co-occurrence Matrix (GLCM) and the backscatter coefficient. This was because of how well it picked up on differences in texture between burned and unburned scars. In contrast, Sentinel-2 optical remote sensing was employed to evaluate the extent of the burnt intensity levels for both regions utilizing the differential Normalized Burnt Ratio (dNBR). A Support Vector Machine (SVM) and Markov Random Field (MRF) classifier were utilized to investigate the study's context. The ideal smoothing parameter is the result of incorporating the image's spectral characteristics and spatial meaning. Sentinel-2 images were used as a foundation for both the test and training datasets, which were built from images of both unburned and burned areas broken down pixel by pixel. In both types, including spectral sensitivity and sensitivity of Polarimetric for the two groups identified after classification, the experimental findings showed a clear association between them. The algorithm's efficiency was evaluated using the kappa coefficient and F-score calculation. Except for Sentinel-1 data in Pakistan, all fire areas have more than 0.80 accuracies. The highest precision of both Sentinel-1 and Sentinel-2 was also provided by the performance of users' and producers' accuracy. The entropy alpha decomposition helped define the target given by the H-a plane based on its physical properties. After the burn, the entropy and alpha values diminished and formed a pattern. However, the findings in this field validate the effectiveness of SAR sensors data and optical satellite in forest applications. The related sensitivity is highly dependent on the composition of the landscape, the geographical nature of the study area, and the severity of the burn.