Chronic toxicity of sediment-bound triclosan on freshwater walking catfish Clarias magur: Organ level accumulation and selected enzyme biomarker responses.

Triclosan (TCS) is a biocide widely used in personal care and medicinal products. TCS persists in sediments and has been detected worldwide, making sediments a vital route of TCS exposure to aquatic organisms. This experiment explored the bioaccumulation and toxicological effects of TCS-contaminated sediment. The study revealed that the half-life of TCS in the sediment-water system was 21.52 days. Exposure of Clarias magur juveniles to 0.4 and 0.8 mg kg-1 TCS-spiked sediment resulted in high Biota-Sediment Accumulation Factor (BSAF) with the highest bioaccumulation in the liver (29.62-73.61 mg kg-1), followed by gill (9.22-17.57 mg kg-1), kidney (5.04-9.76 mg kg-1), muscle (2.63-4.87 mg kg-1) and brain (1.53-3.20 mg kg-1). Furthermore, a concentration-dependent increase in oxidative stress biomarkers such as superoxide dismutase (SOD), catalase (CAT) and glutathione-S-transferase (GST) was documented during 45 days of exposure in gill, liver, kidney, muscle, and brain tissues of exposed fish. A similar increasing trend was also recorded for liver transaminase enzymes such as glutamic oxaloacetic transaminase (GOT) and glutamic pyruvic transaminase (GPT) during the experimental period compared to control. Serum biochemical analysis revealed a significant time and concentration-dependent increase in serum glucose, serum GOT, and serum GPT, while serum total protein and albumin decreased significantly during exposure. These findings demonstrate high bioaccumulative and toxic nature of TCS in fish, promoting multiple physiological and biochemical dysfunctions through sediment exposure. The study underscores the urgent need for strengthened regulations and robust monitoring of triclosan across various environmental matrices, including sediment, to mitigate the detrimental impacts of TCS effectively.

PRRT with Lu-177 DOTATATE in Treatment-Refractory Progressive Meningioma: Initial Experience from a Tertiary-Care Neuro-Oncology Center.

PURPOSE: Refractory and/or recurrent meningiomas have poor outcomes, and the treatment options are limited. Peptide receptor radionuclide therapy (PRRT) has been used in this setting with promising results. We have documented our experience of using intravenous (IV) and intra-arterial (IA) approaches of Lu-177 DOTATATE PRRT. METHODS: Eight patients with relapsed/refractory high-grade meningioma received PRRT with Lu-177 DOTATATE by IV and an IA route. At least 2 cycles were administered. Time to progression was calculated from the first PRRT session to progression. The response was assessed on MRI using RANO criteria, and visual analysis of uptake was done on Ga-68 DOTANOC PET/CT. Post-therapy dosimetry calculations for estimating the absorbed dose were performed. RESULTS: Median time to progression was 8.9 months. One patient showed disease progression, whereas seven patients showed stable disease at 4 weeks following 2 cycles of PRRT. Dosimetric analysis showed higher dose and retention time by IA approach. No significant peri-procedural or PRRT associated toxicity was seen. CONCLUSION: PRRT is a safe and effective therapeutic option for relapsed/refractory meningioma. The IA approach yields better dose delivery and should be routinely practised.

Heavy metals and trace minerals in commonly available shark species from North East Arabian Sea: A human health risk perspective.

Shark is a seafood commodity that is a good source of minerals and accumulates heavy metals and trace elements through biomagnification, which can pose health risk if taken above the permissible limit. A study was conducted on commonly landed eleven shark species (Scoliodon laticaudus, Rhizopriodon oligolinx, Sphyrna lewini (CR), Carcharhinus macloti, Carcharinus limbatus, Carcharhinus amblyrhynchoides, Carcharhinus sorrah, Carcharinus falciformes(VU), Glaucostegus granulatus, Chiloscyllium arabicum, Loxodon macrorhinus) and analyzed for their heavy metal content, Hazard Index, Total Hazard Quotient, Metal Pollution Index, and also calculated the health risk associated with the consumption. Most of the heavy metals and trace minerals were found to be within the acceptable limit. The Targeted Hazard Quotient (THQ) and the Hazard Index (HI) of all the species except two were less than 1 (HI ≤ 1.0). The Metal Pollution Index (MPI) is showing either no impact or very low contamination. An overall study on hazard identification and health risk characterization in terms of heavy metals shows contamination of some heavy metals in sharks, but there is no potential human health risk associated with consumption.

External Validation of Robust Radiomic Signature to Predict 2-Year Overall Survival in Non-Small-Cell Lung Cancer.

Lung cancer is the second most fatal disease worldwide. In the last few years, radiomics is being explored to develop prediction models for various clinical endpoints in lung cancer. However, the robustness of radiomic features is under question and has been identified as one of the roadblocks in the implementation of a radiomic-based prediction model in the clinic. Many past studies have suggested identifying the robust radiomic feature to develop a prediction model. In our earlier study, we identified robust radiomic features for prediction model development. The objective of this study was to develop and validate the robust radiomic signatures for predicting 2-year overall survival in non-small cell lung cancer (NSCLC). This retrospective study included a cohort of 300 stage I-IV NSCLC patients. Institutional 200 patients' data were included for training and internal validation and 100 patients' data from The Cancer Image Archive (TCIA) open-source image repository for external validation. Radiomic features were extracted from the CT images of both cohorts. The feature selection was performed using hierarchical clustering, a Chi-squared test, and recursive feature elimination (RFE). In total, six prediction models were developed using random forest (RF-Model-O, RF-Model-B), gradient boosting (GB-Model-O, GB-Model-B), and support vector(SV-Model-O, SV-Model-B) classifiers to predict 2-year overall survival (OS) on original data as well as balanced data. Model validation was performed using 10-fold cross-validation, internal validation, and external validation. Using a multistep feature selection method, the overall top 10 features were chosen. On internal validation, the two random forest models (RF-Model-O, RF-Model-B) displayed the highest accuracy; their scores on the original and balanced datasets were 0.81 and 0.77 respectively. During external validation, both the random forest models' accuracy was 0.68. In our study, robust radiomic features showed promising predictive performance to predict 2-year overall survival in NSCLC.

Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology.

Cancer is a fatal disease and the second most cause of death worldwide. Treatment of cancer is a complex process and requires a multi-modality-based approach. Cancer detection and treatment starts with screening/diagnosis and continues till the patient is alive. Screening/diagnosis of the disease is the beginning of cancer management and continued with the staging of the disease, planning and delivery of treatment, treatment monitoring, and ongoing monitoring and follow-up. Imaging plays an important role in all stages of cancer management. Conventional oncology practice considers that all patients are similar in a disease type, whereas biomarkers subgroup the patients in a disease type which leads to the development of precision oncology. The utilization of the radiomic process has facilitated the advancement of diverse imaging biomarkers that find application in precision oncology. The role of imaging biomarkers and artificial intelligence (AI) in oncology has been investigated by many researchers in the past. The existing literature is suggestive of the increasing role of imaging biomarkers and AI in oncology. However, the stability of radiomic features has also been questioned. The radiomic community has recognized that the instability of radiomic features poses a danger to the global generalization of radiomic-based prediction models. In order to establish radiomic-based imaging biomarkers in oncology, the robustness of radiomic features needs to be established on a priority basis. This is because radiomic models developed in one institution frequently perform poorly in other institutions, most likely due to radiomic feature instability. To generalize radiomic-based prediction models in oncology, a number of initiatives, including Quantitative Imaging Network (QIN), Quantitative Imaging Biomarkers Alliance (QIBA), and Image Biomarker Standardisation Initiative (IBSI), have been launched to stabilize the radiomic features.

Machine-Learning-Based Radiomics for Classifying Glioma Grade from Magnetic Resonance Images of the Brain.

Grading of gliomas is a piece of critical information related to prognosis and survival. Classifying glioma grade by semantic radiological features is subjective, requires multiple MRI sequences, is quite complex and clinically demanding, and can very often result in erroneous radiological diagnosis. We used a radiomics approach with machine learning classifiers to determine the grade of gliomas. Eighty-three patients with histopathologically proven gliomas underwent MRI of the brain. Whenever available, immunohistochemistry was additionally used to augment the histopathological diagnosis. Segmentation was performed manually on the T2W MR sequence using the TexRad texture analysis softwareTM, Version 3.10. Forty-two radiomics features, which included first-order features and shape features, were derived and compared between high-grade and low-grade gliomas. Features were selected by recursive feature elimination using a random forest algorithm method. The classification performance of the models was measured using accuracy, precision, recall, f1 score, and area under the curve (AUC) of the receiver operating characteristic curve. A 10-fold cross-validation was adopted to separate the training and the test data. The selected features were used to build five classifier models: support vector machine, random forest, gradient boost, naive Bayes, and AdaBoost classifiers. The random forest model performed the best, achieving an AUC of 0.81, an accuracy of 0.83, f1 score of 0.88, a recall of 0.93, and a precision of 0.85 for the test cohort. The results suggest that machine-learning-based radiomics features extracted from multiparametric MRI images can provide a non-invasive method for predicting glioma grades preoperatively. In the present study, we extracted the radiomics features from a single cross-sectional image of the T2W MRI sequence and utilized these features to build a fairly robust model to classify low-grade gliomas from high-grade gliomas (grade 4 gliomas).

Systematic review and meta-analysis of prediction models used in cervical cancer.

BACKGROUND: Cervical cancer is one of the most common cancers in women with an incidence of around 6.5 % of all the cancer in women worldwide. Early detection and adequate treatment according to staging improve the patient's life expectancy. Outcome prediction models might aid treatment decisions, but a systematic review on prediction models for cervical cancer patients is not available. DESIGN: We performed a systematic review for prediction models in cervical cancer following PRISMA guidelines. Key features that were used for model training and validation, the endpoints were extracted from the article and data were analyzed. Selected articles were grouped based on prediction endpoints i.e. Group1: Overall survival, Group2: progression-free survival; Group3: recurrence or distant metastasis; Group4: treatment response; Group5: toxicity or quality of life. We developed a scoring system to evaluate the manuscript. As per our criteria, studies were divided into four groups based on scores obtained in our scoring system, the Most significant study (Score > 60 %); Significant study (60 % > Score > 50 %); Moderately Significant study (50 % > Score > 40 %); least significant study (score < 40 %). A meta-analysis was performed for all the groups separately. RESULTS: The first line of search selected 1358 articles and finally 39 articles were selected as eligible for inclusion in the review. As per our assessment criteria, 16, 13 and 10 studies were found to be the most significant, significant and moderately significant respectively. The intra-group pooled correlation coefficient for Group1, Group2, Group3, Group4, and Group5 were 0.76 [0.72, 0.79], 0.80 [0.73, 0.86], 0.87 [0.83, 0.90], 0.85 [0.77, 0.90], 0.88 [0.85, 0.90] respectively. All the models were found to be good (prediction accuracy [c-index/AUC/R2] >0.7) in endpoint prediction. CONCLUSIONS: Prediction models of cervical cancer toxicity, local or distant recurrence and survival prediction show promising results with reasonable prediction accuracy [c-index/AUC/R2 > 0.7]. These models should also be validated on external data and evaluated in prospective clinical studies.

Large Anorectal Venous Malformation Treated With Microfoam Therapy.

Deficiency of smooth muscle cells can lead to dysfunction and engorgement of blood vessels termed as hemangioma, arteriovenous malformations, and venous malformations (VMs). Anorectal VM is a rare disease. It can present with massive hematochezia. An optimal treatment of anorectal VMs has not been defined. Surgery is an option if the lesion can be resected completely. Endoscopic injection sclerotherapy has been reported to be effective in treating small colorectal VMs. However, it has rarely been described in the treatment of large VMs. In this study, we describe a rare case of large anorectal VMs treated with microfoam sclerotherapy.

Clinical Concept-Based Radiology Reports Classification Pipeline for Lung Carcinoma.

Rising incidence and mortality of cancer have led to an incremental amount of research in the field. To learn from preexisting data, it has become important to capture maximum information related to disease type, stage, treatment, and outcomes. Medical imaging reports are rich in this kind of information but are only present as free text. The extraction of information from such unstructured text reports is labor-intensive. The use of Natural Language Processing (NLP) tools to extract information from radiology reports can make it less time-consuming as well as more effective. In this study, we have developed and compared different models for the classification of lung carcinoma reports using clinical concepts. This study was approved by the institutional ethics committee as a retrospective study with a waiver of informed consent. A clinical concept-based classification pipeline for lung carcinoma radiology reports was developed using rule-based as well as machine learning models and compared. The machine learning models used were XGBoost and two more deep learning model architectures with bidirectional long short-term neural networks. A corpus consisting of 1700 radiology reports including computed tomography (CT) and positron emission tomography/computed tomography (PET/CT) reports were used for development and testing. Five hundred one radiology reports from MIMIC-III Clinical Database version 1.4 was used for external validation. The pipeline achieved an overall F1 score of 0.94 on the internal set and 0.74 on external validation with the rule-based algorithm using expert input giving the best performance. Among the machine learning models, the Bi-LSTM_dropout model performed better than the ML model using XGBoost and the Bi-LSTM_simple model on internal set, whereas on external validation, the Bi-LSTM_simple model performed relatively better than other 2. This pipeline can be used for clinical concept-based classification of radiology reports related to lung carcinoma from a huge corpus and also for automated annotation of these reports.

Analysis of image quality by regulating beta function of BSREM reconstruction algorithm and comparison with conventional reconstructions in carcinoma breast studies of PET CT with BGO detector.

BACKGROUND: The study aimed to evaluate the beta penalization factor of the BSREM reconstruction algorithm on a five-ring BGO-based PET CT system and compared it with conventional reconstructions. METHODS: Retrospective study involves 30 breast cancer patient data of 18F-fluorodeoxyglucose ( 18 F-FDG) PET CT for reconstruction with OSEM, OSEM + PSF, and BSREM under variable ß factors ranging from 200 to 600 in the steps of 50. Liver noise, lesion SUVmax, SBR, and SNR for each reconstruction were calculated. Quantitative parameters of each beta factor of BSREM were compared with OSEM and OSEM + PSF, using the Wilcoxon sign rank test with Bonferroni correction, a value of P < 0.002 was considered statistically significant. Visual scoring by two readers was also evaluated. RESULTS: Thirty lesions of mean size 1.91 ± 0.58 cm range (0.7-3.6 cm) were identified. Liver noise and SBR were reduced, whereas SNR was increased with an increasing ß value of BSREM. In comparison with OSEM, liver noise was not significantly different from ß200 and ß250. SNR of OSEM was significantly lower than any other ß factors and SBR of ß factor less than 500 was significantly higher than OSEM. In comparison with OSEM + PSF, liver noise was not significantly different from ß400 and ß350-500 do not show a significant difference in SNR and SBR compared with OSEM + PSF. ß350 scored highest under visual scoring with a moderate agreement. CONCLUSION: The study quantitatively indicates the optimum beta range of ß250-450 and the qualitative evaluation indicates that ß350 is an optimum beta factor of BSREM in breast cancer cases for 18 F-FDG WB-PET CT.

Assessment of aeolian dust concentration, elemental composition, and their wet and dry deposition fluxes over the Northeast Arabian Sea.

Atmospheric aerosol over the Arabian Sea is significantly impacted by the long-range transported mineral dust from the surrounding continents. This transported mineral dust is hypothesized and tested during several studies to see the impacts on the surface ocean biogeochemical processes and subsequently to the Carbon cycle. It is, thus important to quantify dust contributions and their fluxes to the Arabian Sea. Here we assess temporal variability of dust concentration, their elemental characteristics as well as quantify their dry and wet deposition fluxes over the North-eastern Arabian Sea. The dust concentrations were found to vary from 59 to 132 µg m-3 which accounts for 50% to 90% of total mass during dusty days. However, its contribution during pre and post dust storms ranges between 6% and 60%. Relatively higher dust dry deposition flux of 28 ± 7 mg m-2 day-1 (range: 20-44) is estimated for dusty days compared to pre and post dusty days (range: 0.4-22 mg m-2 day-1). In contrast to dry deposition fluxes, significantly higher fluxes are estimated from wet deposition, averaging around 240 ± 220 mg m-2 day-1. These values are five times higher than those reported from cruise samples collected over the Arabian Sea. The contribution of dust to aerosol mass is further ascertained using elemental composition, wherein a significant correlation was observed between Fe and Al (r2 = 0.77) for samples collected during the dusty period, highlighting their similar crustal sources. Our estimation of dust flux over this region has implications for the supply of nutrients associated with natural dust to the surface water of the Arabian Sea.Implications: The Arabian Sea, one of the productive oceanic regions among the global oceans, has been identified as a perennial source of atmospheric CO2. This basin is heavily impacted by atmospheric dust deposition/inputs owing to its geographical location being surrounded by arid and semi-arid regions. It has been hypothesized that aeolian dust plays a significant role in modulating surface water biogeochemical processes including primary productivity, in the Arabian Sea. Furthermore, modelling studies have highlighted on the role of dust (containing Fe) in fueling and enhancing primary productivity in the Arabian Sea. However, quantification of dust deposition fluxes (wet and dry) on seasonal time scale is missing in the literature. This paper aims to partially fulfil this research gap by providing a long-term data of wet and dry deposition fluxes over the northeastern Arabian Sea. We have also discussed their seasonal variability and factors affecting this flux. Thus, this study will be valuable contribution to the aeolian research community and have significant implication toward the role of aeolian deposition to the surface water biogeochemical processes in the Arabian Sea.

Radiomics: a quantitative imaging biomarker in precision oncology.

Cancer treatment is heading towards precision medicine driven by genetic and biochemical markers. Various genetic and biochemical markers are utilized to render personalized treatment in cancer. In the last decade, noninvasive imaging biomarkers have also been developed to assist personalized decision support systems in oncology. The imaging biomarkers i.e., radiomics is being researched to develop specific digital phenotype of tumor in cancer. Radiomics is a process to extract high throughput data from medical images by using advanced mathematical and statistical algorithms. The radiomics process involves various steps i.e., image generation, segmentation of region of interest (e.g. a tumor), image preprocessing, radiomic feature extraction, feature analysis and selection and finally prediction model development. Radiomics process explores the heterogeneity, irregularity and size parameters of the tumor to calculate thousands of advanced features. Our study investigates the role of radiomics in precision oncology. Radiomics research has witnessed a rapid growth in the last decade with several studies published that show the potential of radiomics in diagnosis and treatment outcome prediction in oncology. Several radiomics based prediction models have been developed and reported in the literature to predict various prediction endpoints i.e., overall survival, progression-free survival and recurrence in various cancer i.e., brain tumor, head and neck cancer, lung cancer and several other cancer types. Radiomics based digital phenotypes have shown promising results in diagnosis and treatment outcome prediction in oncology. In the coming years, radiomics is going to play a significant role in precision oncology.

Installation and Optimization of 68Ga Synthesis Module for Clinical Use: An Institutional Experience.

INTRODUCTION: With advent of gallium-labeling somatostatin analogs and its evaluation under positron emission tomography-computed tomography, there has been a tremendous surge in its application. Gallium 68 can be made available either from onsite cyclotron production or in the form of ready-to-use 68Ge/68Ga generators. Wherein setting up and running of cyclotron amounts to huge investment and dedicated team, the 68Ge/68Ga generator has proved to be a better option and viable project. Moreover, due to long half-life of 68Ge, i.e. 271 days, it enables the usage of generator for several months. The preparation of gallium-labeled peptides is much simpler in comparison to 18F radiochemistry, but the radiation exposure has always been an area of concern owing to high-energy annihilation photon of 511 keV. MATERIALS AND METHODS: In this study, we share our experience of self-installation of 68Ge/68Ga generator during lockdown and the various steps involved in installation of fully automated peptide-labeling system in customized mini hot cell module, synthesis steps, and quality control steps of gallium-based radiopharmaceutical. RESULTS: The installation was successfully completed with online assistance during the pandemic situation. The average elution yield met company specification (>80%), and 68Ga-labeled peptides were prepared with high radiochemical purity (>95%). The overall exposure in single batch of production and quality control never exceeded 3 µSv as shielding was well-taken care of with customized mini hot cell. CONCLUSION: With the described experience and validation process, one can easily think of making an installation at his/her center and cater to the needs of society with a new radiopharmaceutical.

Torsion of spleen and portal hypertension: Pathophysiology and clinical implications.

The displacement of spleen from its normal location to other places is known as wandering spleen (WS) and is a rare disease. The repeated torsion of WS is due to the presence of long pedicle and absence/laxity of anchoring ligaments. A WS is an extremely rare cause of left-sided portal hypertension (PHT) and severe gastric variceal bleeding. Left-sided PHT usually occurs as a result of splenic vein occlusion caused by splenic torsion, extrinsic compression of the splenic pedicle by enlarged spleen, and splenic vein thrombosis. There is a paucity of data on WS-related PHT, and these data are mostly in the form of case reports. In this review, we have analyzed the data of 20 reported cases of WS-related PHT. The mechanisms of pathogenesis, clinico-demographic profile, and clinical implications are described in this article. The majority of patients were diagnosed in the second to third decade of life (mean age: 26 years), with a strong female preponderance (M:F = 1:9). Eleven of the 20 WS patients with left-sided PHT presented with abdominal pain and mass. In 6 of the 11 patients, varices were detected incidentally on preoperative imaging studies or discovered intraoperatively. Therefore, pre-operative search for varices is required in patients with splenic torsion.

Importance of Understanding and Analyzing Daily Quality Assurance Test of Positron Emission Tomography/Computed Tomography Equipment in Minimizing the Downtime of Equipment in Remote Places.

This article briefly describes the event of a defective detector block in a daily quality assurance scan/blank scan and insists on implementing guidelines to scan or not to scan in such a scenario. The nuclear medicine physicist should have a clear understanding of the blank scan graph, which shall help rectify the right cause of problem and give confidence to the physician in reporting the acquired study. A routine blank scan in positron emission tomography signifies various parameters of the crystal (coincidence count rate, single count rate, dead time, and coincidence time along with energy response) and in some respect is analogous to the daily uniformity flood image for gamma cameras, providing an overall assessment of detector response. We encountered a bad detector block in our routine quality assurance scan/blank scan and analyzed the root cause behind such an error which was finally restored to normalcy by replacing the defected part with a new one and an error-free blank scan was established. The analysis was carried out by performing various possible checks and discussing the issue with service engineer to help identify the defects much before service engineer actually arrived in our department. This allowed us to take the correct decision and enabled us to get the scanner repaired faster. Hence, a good understanding of the daily quality control test and proper analysis of the same may result in swift decision-making and faster repair of equipment leading to minimal disruption in the clinical workflow as well as avoidance of suboptimal scanning leading to the wrong diagnosis.

Neurological manifestations of hepatitis E virus infection: An overview.

Hepatitis E virus (HEV) is an important cause of repeated waterborne outbreaks of acute hepatitis. Recently, several extrahepatic manifestations (EHMs) have been described in patients with HEV infection. Of these, neurological disorders are the most common EHM associated with HEV. The involvement of both the peripheral nervous system and central nervous system can occur together or in isolation. Patients can present with normal liver function tests, which can often be misleading for physicians. There is a paucity of data on HEV-related neurological manifestations; and these data are mostly described as case reports and case series. In this review, we analyzed data of 163 reported cases of HEV-related neurological disorders. The mechanisms of pathogenesis, clinico-demographic profile, and outcomes of the HEV-related neurological disorders are described in this article. Nerve root and plexus disorder were found to be the most commonly reported disease, followed by meningoencephalitis.

Endoscopic Mucosectomy: A Novel Technique for Management of Polypoidal Solitary Rectal Ulcer Syndrome.

Solitary rectal ulcer syndrome (SRUS) is an uncommon disorder often challenging to treat. Surgical treatment is associated with suboptimal outcomes and postoperative complications. Argon plasma coagulation helps control rectal bleeding and healing of ulcers, but more extended follow-up data are not available. The macroscopic appearance of SRUS can be polypoid in 17%-25% of cases. Here, we describe a novel endoscopic technique for treating symptomatic patients with polypoidal variant of SRUS after failed medical and endoscopic argon plasma coagulation treatments.

Emerging role of artificial intelligence in nuclear medicine.

The role of artificial intelligence is increasing in all branches of medicine. The emerging role of artificial intelligence applications in nuclear medicine is going to improve the nuclear medicine clinical workflow in the coming years. Initial research outcomes are suggestive of increasing role of artificial intelligence in nuclear medicine workflow, particularly where selective automation tasks are of concern. Artificial intelligence-assisted planning, dosimetry and procedure execution appear to be areas for rapid and significant development. The role of artificial intelligence in more directly imaging-related tasks, such as dose optimization, image corrections and image reconstruction, have been particularly strong points of artificial intelligence research in nuclear medicine. Natural Language Processing (NLP)-based text processing task is another area of interest of artificial intelligence implementation in nuclear medicine.

Plastic versus metal stents for inoperable gallbladder cancer with hilar biliary obstruction: the jury is still out.

In unresectable malignant hilar obstruction, adequate biliary drainage can be achieved with endoscopic placement of plastic or metal stents. Stent patency and patient survival may differ, depending on the primary disease, disease progression and stent type. Metal and plastic stents were compared in patients with malignant hilar strictures in several studies, but these studies mainly included patients who had cholangiocarcinoma, without taking into consideration potential differences in the invasion properties of tumor cells, histological differentiation and the biological behavior of different tumors. Gallbladder cancer (GBC) is the most common malignancy of the biliary tract, especially in the Indian subcontinent and Latin America. About half the patients with GBC present with jaundice, which usually means the tumor is inoperable. Palliative endoscopic stenting remains the first-line treatment of unresectable GBC with biliary obstruction. Primary disease progression is faster in GBC compared to cholangiocarcinoma. There is a paucity of data on the selection of stents for inoperable GBC with hilar biliary obstruction. This review focuses on the published literature related to the selection of stents for unresectable GBC with hilar obstruction.

Baseline serum cystatin C as a marker of acute kidney injury in patients with acute-on-chronic liver failure.

BACKGROUND: A creatinine-based estimation of the renal function lags behind the onset of disease process. Cystatin C is a new marker for acute kidney injury (AKI). However, data are limited in patients with acute-on-chronic liver failure (ACLF). We evaluated serum cystatin C as an early predictor of AKI in patients with ACLF. METHODS: In a prospective observational study, patients with ACLF and normal serum creatinine level were included in the study. Serum cystatin C was analyzed with the development of AKI and the disease outcome. RESULT: Forty-seven patients (mean age: 43.26±16.34 years; male:female: 2.35:1) were included in the study. AKI developed in 34% of patients during the hospital stay. Receiver operating characteristic (ROC) curve analysis revealed that the best cutoff for baseline cystatin C was 1.47 mg/L with a sensitivity of 0.94 and specificity of 0.68. The cystatin C ((area under the curve [AUC]=0.853) performance was better than that of the creatinine (AUC=0.699), Child-Turcotte-Pugh (CTP) (AUC=0.661), and model for end-stage liver disease-sodium (MELD-Na) (AUC=0.641). In the univariate analysis, age, platelet count, creatinine, estimated glomerular filtration rate (eGFR)-modification of diet in renal disease (MDRD), cystatin C, and estimated glomerular filtration rate-serum cystatin C (eGFRcysC) were significantly associated with AKI in ACLF patients. Cystatin C was an independent positive predictor of AKI. Cystatin C was positively correlated with the MELD-Na scores (r=0.374 and p=0.009). CONCLUSION: Our study supports previous studies reporting that serum cystatin C is a better predictor for AKI development compared to serum creatinine. Cystatin C may be used as an early marker for new-onset AKI in hospitalized patients with ACLF.