Metastatic Fibrosarcomatous Transformation of Dermatofibrosarcoma Protuberans With Lung Metastasis: A Case Report.

Dermatofibrosarcoma protuberans (DFSP) is a low- to intermediate-grade dermal soft tissue malignant tumor (sarcoma) with a high local recurrence rate but low metastatic potential. DFSP is characterized by uniform spindle cell fascicles arranged classically in a storiform pattern and by CD34 immunoreactivity. On gross examination, DFSP usually manifests as a white or yellow soft tissue mass with a smooth outer surface and poor circumscription. In this study, we report a case of DFSP with fibrosarcomatous transformation, a rare but well-known phenomenon encountered in DFSP that is correlated with an increased risk of adverse outcomes in patients with DFSP. A 45-year-old male presented with a progressively enlarging lump on his left shoulder, initially suspected of being a lipoma but diagnosed as a fibrosarcomatous transformation of DFSP. Surgical resection was performed, with the subsequent identification of metastatic sarcoma in pulmonary nodules. Robotic-assisted thoracoscopy excised the nodules, confirming metastatic sarcoma with aggressive behavior. Despite negative adjuvant treatment plans, the patient remains under surveillance with imaging, showing no recurrence in recent scans. Continued follow-up with medical and surgical oncology is planned. DFSP is a rare soft tissue sarcoma characterized by indolent growth and low metastatic potential, except in fibrosarcomatous transformation cases. Molecularly, DFSP is defined by a COL1A1-PDGFB fusion transcript that is targetable with imatinib therapy. Treatment involves wide surgical resection, with adjuvant radiation therapy in select cases. Radiation therapy may be employed in cases with close or positive margins, while conventional chemotherapy has limited utility. Multidisciplinary collaboration is crucial for optimal management. Overall, this case underscores the challenges in diagnosing and managing aggressive sarcomas like fibrosarcomatous DFSP, emphasizing the importance of vigilant surveillance and multidisciplinary collaboration in optimizing patient outcomes. Further research is needed to understand the mechanisms underlying fibrosarcomatous transformation and to explore novel therapeutic avenues for this challenging malignancy.

Beyond Midostaurin: Role of Avapritinib in Managing Systemic Mastocytosis.

We present a case of an adult male who presented with pancytopenia accompanied by symptomatic anemia, necessitating chronic transfusions. He was diagnosed with systemic mastocytosis with an associated hematologic neoplasm. Following an inadequate response to midostaurin therapy, the patient was initiated on the newly approved avapritinib. The patient showed significant improvements in all three blood cell lines; however, he developed leg edema, blepharedema, and gum bleeding on this medication. This case underscores the intricacies of managing a patient with advanced systemic mastocytosis, the emerging role of highly selective KIT inhibition in its treatment, and the practical management of adverse medication effects.

B cells in perivascular and peribronchiolar granuloma-associated lymphoid tissue and B-cell signatures identify asymptomatic Mycobacterium tuberculosis lung infection in Diversity Outbred mice.

Because most humans resist Mycobacterium tuberculosis infection, there is a paucity of lung samples to study. To address this gap, we infected Diversity Outbred mice with M. tuberculosis and studied the lungs of mice in different disease states. After a low-dose aerosol infection, progressors succumbed to acute, inflammatory lung disease within 60 days, while controllers maintained asymptomatic infection for at least 60 days, and then developed chronic pulmonary tuberculosis (TB) lasting months to more than 1 year. Here, we identified features of asymptomatic M. tuberculosis infection by applying computational and statistical approaches to multimodal data sets. Cytokines and anti-M. tuberculosis cell wall antibodies discriminated progressors vs controllers with chronic pulmonary TB but could not classify mice with asymptomatic infection. However, a novel deep-learning neural network trained on lung granuloma images was able to accurately classify asymptomatically infected lungs vs acute pulmonary TB in progressors vs chronic pulmonary TB in controllers, and discrimination was based on perivascular and peribronchiolar lymphocytes. Because the discriminatory lesion was rich in lymphocytes and CD4 T cell-mediated immunity is required for resistance, we expected CD4 T-cell genes would be elevated in asymptomatic infection. However, the significantly different, highly expressed genes were from B-cell pathways (e.g., Bank1, Cd19, Cd79, Fcmr, Ms4a1, Pax5, and H2-Ob), and CD20+ B cells were enriched in the perivascular and peribronchiolar regions of mice with asymptomatic M. tuberculosis infection. Together, these results indicate that genetically controlled B-cell responses are important for establishing asymptomatic M. tuberculosis lung infection.

Clinical Presentation, Tumor Characteristics, and Management of Intradiverticular Transitional Cell Carcinoma of the Urinary Bladder: A Systematic Review.

Intradiverticular transitional cell carcinoma (TCC) of the bladder poses unique challenges due to its presentation within the bladder diverticula. This review synthesizes current knowledge on the diagnosis and management of this condition, emphasizing the need for early detection to optimize patient outcomes. The literature underscores the importance of tailored treatment strategies, ranging from radical surgeries to adjuvant chemotherapy, to combat the aggressive nature of intradiverticular TCC. Additionally, stringent post-treatment surveillance protocols are vital in addressing high recurrence rates. Future research directions include biomarker identification, comparative efficacy studies of treatment modalities, and the exploration of innovative therapeutic approaches such as immunotherapy. Longitudinal studies analyzing patient outcomes will provide valuable insights into survival rates and quality of life post-treatment, informing future clinical guidelines. This comprehensive review aims to enhance understanding and management strategies for intradiverticular TCC, paving the way for improved patient care and outcomes in this challenging form of bladder cancer.

Hypercalcemia and Bone Metastasis in a Case of Large Cell Neuroendocrine Carcinoma With Unknown Primary.

Large cell neuroendocrine carcinoma (LCNEC) constitutes a rare subset of highly undifferentiated malignancies known for their aggressive nature. Although these tumors commonly originate in the lungs and gastrointestinal tract, their potential occurrence is not restricted to specific anatomical sites, giving rise to a variety of symptoms. Notably, cases of neuroendocrine tumors (NETs) with an unidentified primary source exhibit a graver prognosis and shorter survival periods compared to those with clearly identified origins. NETs frequently demonstrate a propensity to metastasize, spreading to diverse anatomical regions such as the liver, lungs, lymph nodes, and bones, illustrating their aggressive nature and the complexity of their management. In this context, we present the case of a 59-year-old male who sought medical attention in the emergency department due to right upper quadrant (RUQ) abdominal pain. Initial diagnostic assessments revealed significantly elevated liver function tests and severe hypercalcemia. A right upper quadrant ultrasound (RUQ US) was subsequently performed, which revealed heterogeneous hepatic echotexture with innumerable echogenic masses, suggesting a metastatic process. A computed tomography (CT) scan was then ordered to evaluate further the RUQ US findings, which showed numerous hypovascular liver masses, raising concerns of malignancy. A liver biopsy confirmed a diagnosis of LCNEC with an unidentified primary source.

Epigastric Pain and Dysphagia in a 36-Year-Old Man Due to Primary Esophageal Small Cell Carcinoma.

BACKGROUND Small cell carcinoma is an aggressive malignant neuroendocrine tumor that most commonly occurs in the lung. Primary small cell carcinoma of the esophagus (PSCCE) is rare and is an aggressive malignancy with poor prognosis and no clear management guidelines. This report describes the case of a 36-year-old man presenting with epigastric pain, dysphagia, and melena due to a primary esophageal small cell carcinoma. CASE REPORT A 36-year-old presented to the Emergency Department (ED) with epigastric pain associated with food intake. Initial workup was unremarkable, and a presumed clinical diagnosis of reflux esophagitis and peptic strictures was made, prompting empiric treatment with anti-secretory therapies. Despite these therapies, he presented to the emergency room with progressively worsening dysphagia. Endoscopic examination (EGD) revealed a large necrotic mass, and computed tomography (CT) imaging revealed liver metastasis. Biopsies from both the liver and esophageal masses confirmed small cell carcinoma. His clinical course was complicated by a broncho-esophageal fistula, leading to massive hemoptysis, necessitating intubation. Unfortunately, his condition deteriorated rapidly, and he chose to pursue hospice care. He died 3 months after his initial presentation. CONCLUSIONS This report has presented a rare case of primary esophageal small cell carcinoma and our approach to management. We highlight the importance of early diagnosis, supported by histopathology, and the need for management guidelines.

Acute cardiovascular complications of disseminated intravascular coagulation in acute myeloid leukemia.

BACKGROUND: Disseminated intravascular coagulation (DIC) is a common complication of all leukemia subtypes, but it is an especially prominent feature of Acute Myeloid Leukemias (AML). DIC complicating AML can lead to a variety of complications, however, its association with acute cardiovascular complications has not been reported before. METHODS: National Inpatient Sample Database was used to procure individuals with AML, and baseline demographics and comorbidities were collected using ICD-10-DM codes. Patients were stratified into those with and without DIC. Greedy propensity matching using R was performed to match the two cohorts in 1:1 ratio on age, gender, and fifteen other baseline comorbidities. Univariate analysis pre and post-match along with binary logistic regression analysis post-match were used to analyze outcomes. RESULTS: Out of a total of 37,344 patients with AML, 996 had DIC. DIC patients were younger, predominantly males, and had lower prevalence of baseline cardiovascular comorbidities. DIC patients had statistically significant higher mortality (30.2 % vs 7.8 %), acute myocardial infarction (5.1 % vs 1.8 %), acute pulmonary edema (2.3 % vs 0.7 %), cardiac arrest (6.4 % vs 0.9 %), and acute DVT/PE (6.6 % vs 2.7 %). Logistic regression model after matching showed similar outcomes along with significantly higher rates of acute heart failure in DIC patients. CONCLUSION: These findings highlight the importance of close cardiovascular monitoring and prompt recognition of complications in AML patients with DIC. The underlying mechanisms involve a complex interplay of procoagulant factors, cytokine release, and endothelial dysfunction. Further studies are needed to develop targeted interventions for prevention and management of these complications.

Predicting response to neoadjuvant chemotherapy for colorectal liver metastasis using deep learning on prechemotherapy cross-sectional imaging.

BACKGROUND AND OBJECTIVES: Deep learning models (DLMs) are applied across domains of health sciences to generate meaningful predictions. DLMs make use of neural networks to generate predictions from discrete data inputs. This study employs DLM on prechemotherapy cross-sectional imaging to predict patients' response to neoadjuvant chemotherapy. METHODS: Adult patients with colorectal liver metastasis who underwent surgery after neoadjuvant chemotherapy were included. A DLM was trained on computed tomography images using attention-based multiple-instance learning. A logistic regression model incorporating clinical parameters of the Fong clinical risk score was used for comparison. Both model performances were benchmarked against the Response Evaluation Criteria in Solid Tumors criteria. A receiver operating curve was created and resulting area under the curve (AUC) was determined. RESULTS: Ninety-five patients were included, with 33,619 images available for study inclusion. Ninety-five percent of patients underwent 5-fluorouracil-based chemotherapy with oxaliplatin and/or irinotecan. Sixty percent of the patients were categorized as chemotherapy responders (30% reduction in tumor diameter). The DLM had an AUC of 0.77. The AUC for the clinical model was 0.41. CONCLUSIONS: Image-based DLM for prediction of response to neoadjuvant chemotherapy in patients with colorectal cancer liver metastases was superior to a clinical-based model. These results demonstrate potential to identify nonresponders to chemotherapy and guide select patients toward earlier curative resection.

Straw incorporation into microplastic-contaminated soil can reduce greenhouse gas emissions by enhancing soil enzyme activities and microbial community structure.

Microplastic (MP) contamination poses a substantial threat to agroecosystems, disrupting soil properties, nutrient cycles, and microbial communities and ultimately affecting plant growth and ecosystem resilience. The effects of straw addition on the storage of soil organic carbon (SOC) and greenhouse gas emissions have been extensively explored, but these effects have not been examined in the context of MP contamination. To assess the impacts of legume straw and polyethylene microplastics on SOC fractions and carbon dioxide (CO2) and nitrous oxide (N2O) emissions, 7-month soil incubation experiments were performed. The results revealed that the inclusion of legume straw in soil considerably increased microbial SOC compared to the control. However, straw addition to MP-contaminated soil reduced microbial SOC compared to that in soil containing only straw. In contrast, the addition of straw to MP-contaminated soil elevated (+44%) the SOC mineral relative to the sole application of straw. Intriguingly, straw incorporation into MP-contaminated soil reduced microbial biomass carbon and nitrogen relative to soil containing only straw. Straw addition to MP-contaminated soil enhanced the nitrification activity and reduced the relative expression of AOBamoABC gene compared to sole straw-incorporated soil and the control. Greenhouse gas emissions were also modulated; for instance, straw incorporation into MP-contaminated soil reduced CO2 and N2O emissions by -11% and -46%, compared to straw incorporation alone. The urease and phosphatase activities were decreased (-58% and -12%) in the MP-polluted soil with straw incorporation compared with those in the soil in which only straw was applied. However, invertase and catalase activities were upregulated in the straw-incorporated soil contaminated with MPs. Straw addition in the MP-polluted soil considerably enhanced (+2%) the microbial community structure (indicated by PLFA) compared to the sole straw application. These results provide a comprehensive perspective on the role of legume straw incorporation in addressing MP pollution, showcasing its potential for sustainable agricultural practices in the face of evolving environmental challenges.

A Meta-Analysis of Randomized Clinical Trials Assessing the Efficacy of PARP Inhibitors in Metastatic Castration-Resistant Prostate Cancer.

Prostate cancer ranks as the second most common malignancy in males. Prostate cancer progressing on androgen deprivation therapy (ADT) is castration-resistant prostate cancer (CRPC). Poly-ADP ribose polymerase (PARP) inhibitors (PARPis) have been at the forefront of the treatment of CRPC. We aim to better characterize the progression-free survival (PFS) and overall survival (OS) in metastatic CRPC patients treated with PARPis. A systemic review search was conducted using National Clinical Trial (NCT), PubMed, Embase, Scopus, and Central Cochrane Registry. The improvement in overall survival was statistically significant, favoring PARPis (hazard ratio (HR) 0.855; 95% confidence interval (CI) 0.752-0.974; p = 0.018). The improvement in progression-free survival was also statistically significant, with results favoring PARPis (HR 0.626; 95%CI 0.566-0.692; p = 0.000). In a subgroup analysis, similar results were observed where the efficacy of PARPis was evaluated in a subgroup of patients without homologous recombination repair (HRR) gene mutation, which showed improvement in PFS favoring PARPis (HR 0.747; 95%CI 0.0.637-0.877; p = 0.000). Our meta-analysis of seven RCTs showed that PARPis significantly increased PFS and OS when used with or without antihormonal agents like abiraterone or enzalutamide.

Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images.

Multiple instance learning (MIL) models have achieved remarkable success in analyzing whole slide images (WSIs) for disease classification problems. However, with regard to giga-pixel WSI classification problems, current MIL models are often incapable of differentiating a WSI with extremely small tumor lesions. This minute tumor-to-normal area ratio in a MIL bag inhibits the attention mechanism from properly weighting the areas corresponding to minor tumor lesions. To overcome this challenge, we propose salient instance inference MIL (SiiMIL), a weakly-supervised MIL model for WSI classification. We introduce a novel representation learning for histopathology images to identify representative normal keys. These keys facilitate the selection of salient instances within WSIs, forming bags with high tumor-to-normal ratios. Finally, an attention mechanism is employed for slide-level classification based on formed bags. Our results show that salient instance inference can improve the tumor-to-normal area ratio in the tumor WSIs. As a result, SiiMIL achieves 0.9225 AUC and 0.7551 recall on the Camelyon16 dataset, which outperforms the existing MIL models. In addition, SiiMIL can generate tumor-sensitive attention heatmaps that is more interpretable to pathologists than the widely used attention-based MIL method. Our experiments imply that SiiMIL can accurately identify tumor instances, which could only take up less than 1% of a WSI, so that the ratio of tumor to normal instances within a bag can increase by two to four times.

Alleviation of cadmium toxicity in wheat by strigolactone: Regulating cadmium uptake, nitric oxide signaling, and genes encoding antioxidant defense system.

Cadmium (Cd) in the food system poses a serious threat to human health. The evidence on strigolactones-mediated alleviation of abiotic stress signaling and eliciting physiological modifications in plants is scarce. Therefore, this experiment was conducted to explore the role of exogenous applied strigolactone (SL) in alleviating the toxic effects of Cd and to unravel its physiological, biochemical, and molecular mechanisms in wheat. Excessive accumulation of Cd drastically reduces growth attributes (-15%), nitric oxide signaling, and photosynthetic pigments by increasing oxidative stress biomarkers. Foliar applied SL (4 µM) decreased the Cd-induced growth inhibition (+10%), lessened plant Cd contents (-38% and -36%), shielded chlorophyll pigments (+25%), and considerably decreased Cd-induced oxidative stress in wheat. Moreover, SL applied on wheat foliage remarkably enhanced shoot and root nitric oxide content (+122% and +156%) and nitric oxide synthase activity (104% and 92%) in wheat, efficiently mitigating the Cd-induced suppression of superoxide dismutase and peroxidase, elevating the expression of genes encoding antioxidant defense system. The results of the current research exhibit that SL (GR24) could be a potential candidate for detoxification of Cd by reducing Cd contents, elevating the expression of genes encoding antioxidant defense system, and protecting wheat plants from oxidative stress by indirectly reducing oxidative stress biomarkers andsubsequently contributing to decreasing the possible risk of Cd contamination.

Association Of Biochemical Markers With COVID-19 Severity In Pakistan.

Objective: To evaluate demographics, biochemical markers and clinical features of patients suffering from coronavirus disease-2019. METHODS: The cross-sectional study was conducted retrospectively at the Capital Hospital, Islamabad, and the Fauji Foundation Hospital, Rawalpindi, Pakistan, from October 08, 2021 to March 01, 2022 and comprised patients of either gender with coronavirus disease-2019 diagnosed on the basis of reverse transcriptase polymerase chain reaction. Patients' demographic, clinical and laboratory findings were obtained using patient charge sheets. Coronavirus disease-2019 was categorised as non-severe, severe and critical, according to the World Health Organisation criteria. Data was analysed using SPSS 26. RESULTS: Of the 431 patients, 91(21.1%) were men and 340(78.9%) were women. The overall mean age was 60.75±14.45 years. Of the total, 148(34.3%) had non-severe, 190(44.1%) severe and 93(21.6%) had critical condition at the time of admission. Hypertension 307(71.2%) and diabetes mellitus 249(57.8%) were the most common comorbidities, while fever 353(81.9%), shortness of breath 339(78.7%) and cough 302(70.1%) were the most common symptoms reported. Higher age was significantly associated with coronavirus disease-2019 severity (p<0.001). Among comorbidities, chronic kidney disease (p<0.001) and cancer (p=0.046), and, among signs and symptoms, shortness of breath (p=0.002) and chest pain (p=0.021), were significantly associated with coronavirus disease-2019 severity. Serum total bilirubin, alanine aminotransferase, urea and creatinine levels had significant association with disease severity (p<0.001). Total leukocyte count, neutrophil-to-lymphocyte ratio, prothrombin time, and plasma D-Dimer levels had significant association with disease severity (p<0.001). Serum ferritin, lactate dehydrogenase and interleukin-6 levels were also significantly associated with disease severity (p<0.05). Conclusion: Assessment of biochemical markers was an excellent way to monitor disease progression in coronavirus disease-2019 patients.

Development of ZnO-GO-NiO membrane for removal of lead and cadmium heavy metal ions from wastewater.

The presence of heavy metal (HM) ions, such as lead, cadmium, and chromium in industrial wastewater discharge are major contaminants that pose a risk to human health. These HMs should separate from the wastewater to ensure the reuse of the discharged water in the process and mitigate their environmental impacts. The distinctive mechanical properties of 2D graphene oxide (GO), and the antifouling characteristics of metal oxides (ZnO/NiO) nanoparticles combined to produce composites supporting special features for wastewater treatment. This study employed solution casting and phase inversion methods to synthesize PSF-based GO, ZnO-GO, and ZnO-GO-NiO mixed matrix membranes and the effects of variation in composition on the removal of lead (Pb2+) and cadmium (Cd2+) ion was examined. Several characterization techniques including X-ray diffraction analysis, scanning electron microscopy, energy dispersive X-ray, and Fourier transform infrared spectroscopy were applied to analyze the synthesized NPs and MMMs. The composite membranes were also analyzed in terms of their porosity, permeability, hydrophilicity, surface roughness, zeta potential, thermal stability, mechanical strength, and flux regeneration at various transmembrane pressures (2-3 kgcm-2), and pH value (5.5). The highest adsorption capacities were measured to be 308.16 mg g-1 and 354.80 mg g-1 for Pb (II) and Cd (II), respectively, for membrane (M4_A) having 0.3 wt% of ZnO-GO-NiO nanocomposite, at 200 mg L-1 of feed concentration and 1.60 mL min-1 of permeate flux. The Pb (II) and Cd (II) adsorption breakthrough curves were created, and the results of the experiment were compared with the data of the Thomas model.

BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images.

Breast cancer is the most common malignancy in women, with over 40,000 deaths annually in the United States alone. Clinicians often rely on the breast cancer recurrence score, Oncotype DX (ODX), for risk stratification of breast cancer patients, by using ODX as a guide for personalized therapy. However, ODX and similar gene assays are expensive, time-consuming, and tissue destructive. Therefore, developing an AI-based ODX prediction model that identifies patients who will benefit from chemotherapy in the same way that ODX does would give a low-cost alternative to the genomic test. To overcome this problem, we developed a deep learning framework, Breast Cancer Recurrence Network (BCR-Net), which automatically predicts ODX recurrence risk from histopathology slides. Our proposed framework has two steps. First, it intelligently samples discriminative features from whole-slide histopathology images of breast cancer patients. Then, it automatically weights all features through a multiple instance learning model to predict the recurrence score at the slide level. On a dataset of H&E and Ki67 breast cancer resection whole slides images (WSIs) from 99 anonymized patients, the proposed framework achieved an overall AUC of 0.775 (68.9% and 71.1% accuracies for low and high risk) on H&E WSIs and overall AUC of 0.811 (80.8% and 79.2% accuracies for low and high risk) on Ki67 WSIs of breast cancer patients. Our findings provide strong evidence for automatically risk-stratify patients with a high degree of confidence. Our experiments reveal that the BCR-Net outperforms the state-of-the-art WSI classification models. Moreover, BCR-Net is highly efficient with low computational needs, making it practical to deploy in limited computational settings.

Machine Learning Models for Predicting the Outcomes of Surgical Treatment of Colorectal Liver Metastases.

BACKGROUND: Surgical intervention remains the cornerstone of a multidisciplinary approach in the treatment of colorectal liver metastases (CLM). Nevertheless, patient outcomes vary greatly. While predictive tools can assist decision-making and patient counseling, decades of efforts have yet to result in generating a universally adopted tool in clinical practice. STUDY DESIGN: An international collaborative database of CLM patients who underwent surgical therapy between 2000 and 2018 was used to select 1,004 operations for this study. Two different machine learning methods were applied to construct 2 predictive models for recurrence and death, using 128 clinicopathologic variables: gradient-boosted trees (GBTs) and logistic regression with bootstrapping (LRB) in a leave-one-out cross-validation. RESULTS: Median survival after resection was 47.2 months, and disease-free survival was 19.0 months, with a median follow-up of 32.0 months in the cohort. Both models had good predictive power, with GBT demonstrating a superior performance in predicting overall survival (area under the receiver operating curve [AUC] 0.773, 95% CI 0.743 to 0.801 vs LRB: AUC 0.648, 95% CI 0.614 to 0.682) and recurrence (AUC 0.635, 95% CI 0.599 to 0.669 vs LRB: AUC 0.570, 95% CI 0.535 to 0.601). Similarly, better performances were observed predicting 3- and 5-year survival, as well as 3- and 5-year recurrence, with GBT methods generating higher AUCs. CONCLUSIONS: Machine learning provides powerful tools to create predictive models of survival and recurrence after surgery for CLM. The effectiveness of both machine learning models varies, but on most occasions, GBT outperforms LRB. Prospective validation of these models lays the groundwork to adopt them in clinical practice.

Photocatalysis and adsorption kinetics of azo dyes by nanoparticles of nickel oxide and copper oxide and their nanocomposite in an aqueous medium.

Background: Azo dyes are recalcitrant organic pollutants present in textile industry effluents. Conventional treatment methods to remove them come with a range of disadvantages. Nanoparticles and their nanocomposites offer more efficient, less expensive and easy to handle wastewater treatment alternative. Methods: In this study, nanoparticles of nickel oxide (NiO-NPs), copper oxide (CuO-NPs) and their nanocomposite (NiO/CuO-NC) were synthesized using co-precipitation method. The functional groups present on the surface of synthesized nanomaterials were verified using Fourier-transform infrared spectroscopy (FTIR). Surface morphology was assessed using scanning electron microscopy (SEM) whereas purity, shape and size of the crystallite were determined using X-ray diffraction (XRD) technique. The potential of these nanomaterials to degrade three dyes i.e., Reactive Red-2 (RR-2), Reactive Black-5 (RB-5) and Orange II sodium salt (OII) azo dyes, was determined in an aqueous medium under visible light (photocatalysis). The photodegradation effectiveness of all nanomaterials was evaluated under different factors like nanomaterial dose (0.02-0.1 g 10 mL-1), concentration of dyes (20-100 mg L-1), and irradiation time (60-120 min). They were also assessed for their potential to adsorb RR-2 and OII dyes. Results: Results revealed that at optimum concentration (60 mgL-1) of RR-2, RB-5, and OII dyes, NiO-NPs degraded 90, 82 and 83%, CuO-NPs degraded 49, 34, and 44%, whereas the nanocomposite NiO/CuO-NC degraded 92, 93, and 96% of the said dyes respectively. The nanomaterials were categorized as the efficient degraders of the dyes in the order: NiO/CuO-NC > NiO-NPs > CuO-NPs. The highest degradation potential shown by the nanocomposite was attributed to its large surface area, small particles size, and quick reactions which were proved by advance analytical techniques. The equilibrium and kinetic adsorption of RR-2 and OII on NiO-NPs, CuO-NPs, and NiO/CuO-NC were well explained with Langmuir and Pseudo second order model, respectively (R2 ≥ 0.96). The maximum RR-2 adsorption (103 mg/g) was obtained with NiO/CuO-NC. It is concluded that nanocomposites are more efficient and promising for the dyes degradation from industrial wastewater as compared with dyes adsorption onto individual NPs. Thus, the nanocomposite NiO/CuO-NC can be an excellent candidate for photodegradation as well as the adsorption of the dyes in aqueous media.

Efficacy Of Intra-Articular Ketorolac And Lignocaine On Post-Operative Pain Relief After Arthroscopic Knee Surgery.

BACKGROUND: Arthroscopy of the knee is preferably done under spinal anaesthesia. The optimal analgesia for effective postoperative pain control is important to permit early discharge, comfort and mobility of the patient. Objective of the study is to assess the efficacy of ketorolac and lignocaine administered intra-articularly for postoperative pain following knee arthroscopic surgery. METHODS: A total of 133 patients were randomized into two groups with one group receiving intra-articular Ketorolac and the other group receiving intra-articular Lignocaine. Postoperative pain was then assessed using the Visual Analog Scale (VAS) at 4, 8, 12 and 24 hours after surgery. RESULTS: Both the groups had effective analgesia at 4 hours. The best analgesia was seen in the group that received Ketorolac Intra-articularly and it was found statistically significant. CONCLUSIONS: Administration of intra-articular Ketorolac injection is safe and effective way of achieving postoperative pain relief after arthroscopic knee surgery.

Immunoglobulin D Multiple Myeloma: A Rare Variant.

Immunoglobulin D multiple myeloma (IgD MM) is a rare isotype of multiple myeloma (MM), comprising less than 2% of all cases. It is often associated with advanced disease at the time of diagnosis, an aggressive clinical course, and shorter overall survival (OS) than other subtypes of MM. There is an increased frequency of undetectable or small monoclonal (M-) protein levels on electrophoresis, hypercalcemia, anemia, lytic bony lesions, and renal failure. However, given the rarity of the disease, there are few cases of IgD MM described in the literature. Given the very small amount of IgD immunoglobulins, they may form very small or undetectable M spike on electrophoresis, making the diagnostic error in diagnosing this specific subgroup very easy. Treatment for MM has seen significant advancement, especially over the last decade, with the advent of medications such as proteasome inhibitors, immunomodulatory agents, and monoclonal antibodies. It is important to understand how IgD MM responds to these newer agents and why this disease continues to be associated with poor outcomes despite advancements in treatment. Small clinical studies on patients with IgD MM show better outcomes following a combination of high-dose chemotherapy (HDCT) and autologous stem cell transplant (ASCT) compared to standard chemotherapy. Given the rarity of the disease, there are no large studies done to see the effectiveness of these treatments, and most of the data are derived from small case series. We report a case of IgD kappa MM that was incidentally discovered following a traumatic bicycle accident. The patient started treatment with bortezomib and dexamethasone (Vd) as an inpatient while he was in the rehabilitation unit and was later switched to bortezomib, dexamethasone, and lenalidomide (VRd) as an outpatient. He has now completed seven cycles and successfully underwent autologous hematopoietic stem cell transplantation.

Ki-67 assessment of pancreatic neuroendocrine neoplasms: Systematic review and meta-analysis of manual vs. digital pathology scoring.

Ki-67 assessment is a key step in the diagnosis of neuroendocrine neoplasms (NENs) from all anatomic locations. Several challenges exist related to quantifying the Ki-67 proliferation index due to lack of method standardization and inter-reader variability. The application of digital pathology coupled with machine learning has been shown to be highly accurate and reproducible for the evaluation of Ki-67 in NENs. We systematically reviewed all published studies on the subject of Ki-67 assessment in pancreatic NENs (PanNENs) employing digital image analysis (DIA). The most common advantages of DIA were improvement in the standardization and reliability of Ki-67 evaluation, as well as its speed and practicality, compared to the current gold standard approach of manual counts from captured images, which is cumbersome and time consuming. The main limitations were attributed to higher costs, lack of widespread availability (as of yet), operator qualification and training issues (if it is not done by pathologists), and most importantly, the drawback of image algorithms counting contaminating non-neoplastic cells and other signals like hemosiderin. However, solutions are rapidly developing for all of these challenging issues. A comparative meta-analysis for DIA versus manual counting shows very high concordance (global coefficient of concordance: 0.94, 95% CI: 0.83-0.98) between these two modalities. These findings support the widespread adoption of validated DIA methods for Ki-67 assessment in PanNENs, provided that measures are in place to ensure counting of only tumor cells either by software modifications or education of non-pathologist operators, as well as selection of standard regions of interest for analysis. NENs, being cellular and monotonous neoplasms, are naturally more amenable to Ki-67 assessment. However, lessons of this review may be applicable to other neoplasms where proliferation activity has become an integral part of theranostic evaluation including breast, brain, and hematolymphoid neoplasms.