An unusual case of iatrogenic central venous injury.

Central venous catheter (CVC) provides ready vascular access and is widely used for the performance of hemodialysis. The use of CVC is associated with many complications and one life-threatening complication is central venous injury. We describe an unusual case of central venous injury in a 69-year-old lady with a poorly functioning left internal jugular vein catheter, which was in situ at the time of attempting insertion of a replacement right internal jugular catheter. The management included initial stabilization, urgent hemodialysis, imaging, and an endovascular approach to mitigate the iatrogenic venous injury. The case highlights many learning points. The operator needs to be vigilant for anatomical abnormalities like stenosis in patients who have had previous CVC. In those with central venous perforation, the CVC should be left in situ till a definitive management plan is formulated. An endovascular approach, when feasible, is a minimally invasive effective management strategy.

On the higher-order smallest ring-star network of Chialvo neurons under diffusive couplings.

Network dynamical systems with higher-order interactions are a current trending topic, pervasive in many applied fields. However, our focus in this work is neurodynamics. We numerically study the dynamics of the smallest higher-order network of neurons arranged in a ring-star topology. The dynamics of each node in this network is governed by the Chialvo neuron map, and they interact via linear diffusive couplings. This model is perceived to imitate the nonlinear dynamical properties exhibited by a realistic nervous system where the neurons transfer information through multi-body interactions. We deploy the higher-order coupling strength as the primary bifurcation parameter. We start by analyzing our model using standard tools from dynamical systems theory: fixed point analysis, Jacobian matrix, and bifurcation patterns. We observe the coexistence of disparate chaotic attractors. We also observe an interesting route to chaos from a fixed point via period-doubling and the appearance of cyclic quasiperiodic closed invariant curves. Furthermore, we numerically observe the existence of codimension-1 bifurcation points: saddle-node, period-doubling, and Neimark-Sacker. We also qualitatively study the typical phase portraits of the system, and numerically quantify chaos and complexity using the 0-1 test and sample entropy measure, respectively. Finally, we study the synchronization behavior among the neurons using the cross correlation coefficient and the Kuramoto order parameter. We conjecture that unfolding these patterns and behaviors of the network model will help us identify different states of the nervous system, further aiding us in dealing with various neural diseases and nervous disorders.

Phycobilisome's Exciton Transfer Efficiency Relies on an Energetic Funnel Driven by Chromophore-Linker Protein Interactions.

The phycobilisome is the primary light-harvesting antenna in cyanobacterial and red algal oxygenic photosynthesis. It maintains near-unity efficiency of energy transfer to reaction centers despite relying on slow exciton hopping along a relatively sparse network of highly fluorescent phycobilin chromophores. How the complex maintains this high efficiency remains unexplained. Using a two-dimensional electronic spectroscopy polarization scheme that enhances energy transfer features, we directly watch energy flow in the phycobilisome complex of Synechocystis sp. PCC 6803 from the outer phycocyanin rods to the allophycocyanin core. The observed downhill flow of energy, previously hidden within congested spectra, is faster than timescales predicted by Förster hopping along single rod chromophores. We attribute the fast, 8 ps energy transfer to interactions between rod-core linker proteins and terminal rod chromophores, which facilitate unidirectionally downhill energy flow to the core. This mechanism drives the high energy transfer efficiency in the phycobilisome and suggests that linker protein-chromophore interactions have likely evolved to shape its energetic landscape.

Switching between blebbing and lamellipodia depends on the degree of non-muscle myosin II activity.

Cells can adopt both mesenchymal and amoeboid modes of migration through membrane protrusive activities, namely formation of lamellipodia and blebbing. How the molecular players control the transition between lamellipodia and blebs is yet to be explored. Here, we show that addition of the ROCK inhibitor Y27632 or low doses of blebbistatin, an inhibitor of non-muscle myosin II (NMII) ATPase activity and filament partitioning, induces blebbing to lamellipodia conversion (BLC), whereas addition of low doses of ML7, an inhibitor of myosin light chain kinase (MLCK), induces lamellipodia to blebbing conversion (LBC) in human MDA-MB-231 cells. Similarly, siRNA-mediated knockdown of ROCK and MLCK induces BLC and LBC, respectively. Interestingly, both blebs and lamellipodia membrane protrusions are able to maintain the ratio of phosphorylated to unphosphorylated regulatory light chain at cortices when MLCK and ROCK, respectively, are inhibited either pharmacologically or genetically, suggesting that MLCK and ROCK activities are interlinked in BLC and LBC. Such BLCs and LBCs are also inducible in other cell lines, including MCF7 and MCF10A. These studies reveal that the relative activity of ROCK and MLCK, which controls both the ATPase activity and filament-forming property of NMII, is a determining factor in whether a cell exhibits blebbing or lamellipodia.

Role of proliferation COVID-19 media chatter in predicting Indian stock market: Integrated framework of nonlinear feature transformation and advanced AI.

The outbreak of the COVID-19 pandemic has transpired the global media to gallop with reports and news on the novel Coronavirus. The intensity of the news chatter on various aspects of the pandemic, in conjunction with the sentiment of the same, accounts for the uncertainty of investors linked to financial markets. In this research, Artificial Intelligence (AI) driven frameworks have been propounded to gauge the proliferation of COVID-19 news towards Indian stock markets through the lens of predictive modelling. Two hybrid predictive frameworks, UMAP-LSTM and ISOMAP-GBR, have been constructed to accurately forecast the daily stock prices of 10 Indian companies of different industry verticals using several systematic media chatter indices related to the COVID-19 pandemic alongside several orthodox technical indicators and macroeconomic variables. The outcome of the rigorous predictive exercise rationalizes the utility of monitoring relevant media news worldwide and in India. Additional model interpretation using Explainable AI (XAI) methodologies indicates that a high quantum of overall media hype, media coverage, fake news, etc., leads to bearish market regimes.

Clinical profile and microbiological spectrum of patients with continuous ambulatory peritoneal dialysis-associated peritonitis at a tertiary care center.

Background: We present risk factors, clinical profile, and microbiological spectrum of patients with continuous ambulatory peritoneal dialysis (CAPD)-associated peritonitis who were managed at our center. Methods: All consecutive patients with CAPD-associated peritonitis who presented to our center between July 2018 and December 2019 were included in the study. Risk factors, microbiological spectrum, clinical profile, and outcome of patients were studied. Results: Eighty-five patients with CAPD-associated peritonitis and 50 patients who never had peritonitis during the study period were included. Diabetes Mellitus (OR 0.058, 95% CI0.007-0.493, p < 0.05), residence in rural area (OR 3.376, 95% CI 1.084-10.516, p < 0.05), duration of peritoneal dialysis (OR 0.935, 95% CI 0.886-0.987, p < 0.05), mean serum hemoglobin (OR 1.674, 95% CI 1.119-2.502, p < 0.05) and serum albumin (OR 0.148, 95% CI 0.066-0.333, p < 0.05) were associated with higher risk of peritonitis in CAPD patients. Eight-three patients (98.8%) had turbid CAPD fluid and 52 (61.2%) had fever at the time of presentation to the hospital. The mean CAPD fluid TLC on day 1, 3, and 5 were 2034.3 ± 3330.1 cells/cumm, 1049.0 ± 1210.9 cells/cumm, and 605.2 ± 950.5 cells/cumm, respectively. The organisms isolated were two Escherichia coli (2.4%), one Staphylococcus aureus (1.2%), one Klebsiella (1.2%), two Acinetobacter (2.4%), 10 Fungal organisms (11.7%), and in two (2.4%) cases, Mycobacterium tuberculosis (MTB) was detected by polymerase chain reaction (PCR) of CAPD fluid. Thirty-seven (43.5%) patients had a complete cure and 48 (56.5%) patients were refractory to treatment; hence catheter was removed in them (catheter loss). Six (7.0%) patients died during the study period. Conclusion: CAPD-associated peritonitis is an important risk factor for technique failure. The majority of episodes are culture-negative, and PCR can help in detecting fungal and tubercular peritonitis early in the course.

Brain abscess following kidney transplantation: A single centre observational study from North India.

Background: Amongst the infections in kidney transplant recipients, brain abscess represents an uncommon life-threatening complication. Mortality continues to be high despite improvements in diagnostics and therapeutics. Method: We conducted an observational study, describing the incidence, presentation, implicating pathogen, management and outcome of brain abscess following kidney transplantation at our centre. Result: Amongst the 1492 patients who underwent kidney transplantation at our centre between June 1991 and January 2023 (cumulative follow-up: 4936 patient-years), five females and four males, developed brain abscesses. The incidence proportion (risk) is 0.6% with an incidence rate of 6.03 cases per 1000 patient years. The median duration from transplant to development of brain abscess was 5 weeks (range: 4 weeks to 9 years). The commonest presentation was a headache. A definitive microbiological diagnosis was established in eight out of nine patients. The commonest implicated organism was a dematiaceous fungus, Cladophialophora bantiana (3 patients, 33.3%). Despite the reduction in immunosuppression, surgical evacuation and optimal medical therapy, five (55.55%) patients succumbed to their illness. Conclusions: Brain abscesses following kidney transplantation is an uncommon, life-threatening condition. It usually occurs in the early post-transplant period and the presentation is often subtle. Unlike immunocompetent individuals, a fungus is the most common causative organism in those with solid organ transplants. The management includes a reduction in immunosuppression, early antimicrobial therapy, and surgical decompression.

Parvovirus B19 infection after kidney transplantation: A single centre experience.

Background: Parvovirus B19 is an uncommon cause of anaemia in kidney transplant recipients (KTRs). The study aims to determine the incidence, clinical presentation, laboratory findings and outcome of parvovirus B19-related anaemia in KTR. Method: We conducted a 12-year retrospective, single-centre study describing the clinical profile of KTRs with parvovirus B19-related anaemia. Result: Amongst the 714 patients who underwent kidney transplantation between January 2011 and January 2023, (cumulative follow-up: 1287 patient-years), six females and one male, developed parvovirus B19-related anaemia. The incidence proportion (risk) is 0.98% with an incidence rate of 5.43 cases per 1000 patient-year. The median duration from transplant to development of anaemia was 6 weeks (range: 4-40 weeks). The mean fall in haemoglobin was 2.88 ± 1.55 gm/dl; concomitant leukopenia and thrombocytopenia were observed in 57.1 and 28.6% of patients. Three patients responded to a reduction in immunosuppression, the four non-responders required the administration of low-dose intravenous immunoglobulin. The mean duration from initiation of therapy to a sustained rise in haemoglobin was 7.71 ± 2.62 weeks. None of the patients had a relapse of the infection. Conclusions: Parvovirus B19 infection is an uncommon cause of post-transplant refractory anaemia. The key to successfully managing such patients includes a high index of suspicion, early diagnosis and reduction of immunosuppression with or without administration of intravenous immunoglobulin.

Transcriptome analysis identifies putative multi-gene signature distinguishing benign and malignant pancreatic head mass.

BACKGROUND: Most often, the patients with pancreatic diseases are presented with a mass in pancreatic head region and existing methods of diagnosis fail to confirm whether the head mass is malignant or benign. As subsequent management of the disease hugely depends on the correct diagnosis, we wanted to explore possible biomarkers which could distinguish benign and malignant pancreatic head masses. METHODS: In order to address that gap, we performed a case-control study to identify genome-wide differentially expressed coding and noncoding genes between pancreatic tissues collected from benign and malignant head masses. These genes were next shortlisted using stringent criteria followed by selection of top malignancy specific genes. They subsequently got validated by quantitative RT-PCR and also in other patient cohorts. Survival analysis and ROC analysis were also performed. RESULTS: We identified 55 coding and 13 noncoding genes specific for malignant pancreatic head masses. Further shortlisting and validation, however, resulted in 5 coding genes as part of malignancy specific multi-gene signature, which was validated in three independent patient cohorts of 145 normal and 153 PDAC patients. We also found that overexpression of these genes resulted in survival disadvantage in the patients and ROC analysis identified that combination of 5 coding genes had the AUROC of 0.94, making them potential biomarker. CONCLUSIONS: Our study identified a multi-gene signature comprising of 5 coding genes (CDCA7, DLGAP5, FOXM1, TPX2 and OSBPL3) to distinguish malignant head masses from benign ones.

Modeling hydro, nuclear, and renewable electricity generation in India: An atom search optimization-based EEMD-DBSCAN framework and explainable AI.

Background and objective: Tracking clean electricity generation in developing economies is highly challenging owing to the influence of turbulent external factors. Clean electricity is a significant enabler of striving toward environmental sustainability. In this research, we aim to model hydro, nuclear, and renewable electricity generation in India through applied predictive modeling. We also strive to uncover the influence of the critical determinants responsible for clean electricity growth. Methodology: We propose a granular predictive framework comprising ensemble empirical mode decomposition, clustering applications in spatial data based on density, including noise, and atom search optimization-based novel optimization methodology to predict absolute figures of clean energy generation. The framework uses a series of socio-economic factors reflecting household demand and industrial growth in India as explanatory variables. Results: The rigorous scrutiny of the predictive framework specifies hydro electricity generation is relatively more predictable during the time horizon influenced by the COVID-19 pandemic. The deployment of dedicated explainable artificial intelligence (AI) tools suggests an increased adoption of clean electricity in selected industrial sectors in India, which broadly governs the evolutionary pattern. Conclusion: The underlying research is the first of its kind to fathom the daily temporal dynamics of clean electricity generation in the Indian context. Consideration of three distinct clean electricity sources during highly volatile time regimes underscores the contribution of the work. The predictive framework survives a stringent performance check, which justifies the robustness of the same. Demand in different industrial sectors in India profoundly influences the growth toward clean electricity.

Variation in Lymph Node Assessment for Colon Cancer at the Tumor, Surgeon, and Hospital Level.

BACKGROUND: We hypothesized that tumor- and hospital-level factors, compared with surgeon characteristics, are associated with the majority of variation in the 12 or more lymph nodes (LNs) examined quality standard for resected colon cancer. STUDY DESIGN: A dataset containing an anonymized surgeon identifier was obtained from the National Cancer Database for stage I to III colon cancers from 2010 to 2017. Multilevel logistic regression models were built to assign a proportion of variance in achievement of the 12 LNs standard among the following: (1) tumor factors (demographic and pathologic characteristics), (2) surgeon factors (volume, approach, and margin status), and (3) facility factors (volume and facility type). RESULTS: There were 283,192 unique patient records with 15,358 unique surgeons across 1,258 facilities in our cohort. Achievement of the 12 LNs standard was high (90.3%). Achievement of the 12 LNs standard by surgeon volume was 88.1% and 90.7% in the lowest and highest quartiles, and 86.8% and 91.6% at the facility level for high and low annual volume quartiles, respectively. In multivariate analysis, the following tumor factors were associated with meeting the 12 LNs standard: age, sex, primary tumor site, tumor grade, T stage, and comorbidities (all p < 0.001). Tumor factors were responsible for 71% of the variation in 12 LNs yield, whereas surgeon and facility characteristics contributed 17% and 12%, respectively. CONCLUSIONS: Twenty-nine percent of the variation in the 12 LNs standard is linked to modifiable factors. The majority of variation in this quality metric is associated with non-modifiable tumor-level factors.

Diagnosis of Pancreatic Cancer after Cholecystectomy in the Elderly.

BACKGROUND: Previous studies evaluating whether recent cholecystectomy is associated with a pancreas cancer diagnosis are limited. We aimed to examine if cholecystectomy was performed more frequently in the year prior to cancer diagnosis than would be expected in a similar non-cancer population. METHODS: SEER-Medicare linked files were used to identify patients with pancreatic adenocarcinoma. Cancer diagnoses were considered to be "timely" if within 2 months of cholecystectomy or "delayed" if 2-12 months after cholecystectomy. Clinical factors and survival outcomes were compared using chi-square and Kaplan-Meier analyses. RESULTS: Rate of cholecystectomy in the year prior to diagnosis of cancer was 1.9% for the cancer group, compared to .4% in the non-cancer group (OR = 4.7, 95% CI 4.4-5.1). Differences in the cancer vs non-cancer cohorts at the time of cholecystectomy included a higher age (74 vs 70, P < .0001), more males (49.9% vs 41.7%, P < .0001), and more frequent open technique (21.0% vs 9.4%, P < .0001). Acute pancreatitis was nearly twice as common in the cancer cohort (19.1%) vs the non-cancer cohort (10.7%), P < .0001. There were no differences between patients who had a timely diagnosis after cholecystectomy compared to a delayed diagnosis with regard to age, gender, comorbidity index, race, or rural/urban designation. The rates of localized disease and subsequent resection were also similar between the delayed and timely groups. Overall unadjusted survival was no different between timely and delayed diagnoses, P = .96. DISCUSSION: Elderly patients diagnosed with pancreatic adenocarcinoma are more likely to have had a recent cholecystectomy compared to those without.

Some properties of stop-loss moments under biased sampling.

The stop-loss moments have generally been used as useful summary measures for analyzing the data which exceeds specific threshold levels. In many scientific studies, the investigator cannot record the sampling units with equal probability, and in such a scenario, the selected sample units appear with unequal probability, in other words with different weights, which leads to a biased or weighted sampling. In the present study, we examine the usefulness of stop-loss moments in biased sampling. The application of the weighted stop-loss moments in analyzing biased data has been investigated and compared using different empirical estimators through simulated and real data sets.

Post-partum paraplegia following spinal anaesthesia: a report of two rare cases.

Pregnancy and lumbar puncture are rare instances that can precipitate sudden onset paraplegia in patients with otherwise slow-growing intradural tumours. Surgeons and anaesthesiologists should be aware of the etiological factors leading to pregnancy- and delivery-related rapid tumour growth and its complications. Lumbar puncture-related complications leading to acute precipitation of neurological symptoms must be addressed promptly for favourable outcome in such patients. We describe the report of two patients who developed acute onset paraparesis after spinal anaesthesia for caesarean section. Both were found to be having undiagnosed spinal tumours and managed surgically. We recommend urgent MRI in cases of acute onset non-resolving paraparesis in the peripartum period, for timely diagnosis and management of this rare clinical entity.

Expression of nonmuscle myosin IIC is regulated by non-canonical binding activity of miRNAs.

The expression of mechanoresponsive nonmuscle myosin II (NMII)C is found to be inducible during tumor progression, but its mechanism is yet to be explored. Here, we report a group of microRNAs (mmu-miR-200a-5p, mmu-miR-532-3p, mmu-miR-680, and mmu-miR-1901) can significantly repress the expression of nonmuscle myosin IIC (NMIIC). Interestingly, these microRNAs have both canonical and non-canonical binding sites at 3/UTR and coding sequence (CDS) of NMIIC's heavy chain (HC) mRNA. Each of the miRNA downregulates NMHC-IIC to a different degree as assessed by dual-luciferase and immunoblot analyses. When we abolish the complementary base pairing at canonical binding site, mmu-miR-532-3p can still bind at non-canonical binding site and form Argonaute2 (AGO2)-miRNA complex to downregulate the expression of NMIIC. Modulating the expression of NMIIC by miR-532-3p in mouse mammary tumor cells, 4T1, increases its tumorigenic potential both in vitro and in vivo. Together, these studies provide the functional role of miRNA's non-canonical binding mediated NMIIC regulation in tumor cells.

2D Nano-Sonosensitizers Facilitate Energy Transfer to Enhance Sonodynamic Therapy.

Although sonodynamic therapy (SDT) has shown promise for cancer treatment, the lack of efficient sonosensitizers (SSs) has limited the clinical application of SDT. Here, a new strategy is reported for designing efficient nano-sonosensitizers based on 2D nanoscale metal-organic layers (MOLs). Composed of Hf-oxo secondary building units (SBUs) and iridium-based linkers, the MOL is anchored with 5,10,15,20-tetra(p-benzoato)porphyrin (TBP) sensitizers on the SBUs to afford TBP@MOL. TBP@MOL shows 14.1- and 7.4-fold higher singlet oxygen (1 O2 ) generation than free TBP ligands and Hf-TBP, a 3D nanoscale metal-organic framework, respectively. The 1 O2 generation of TBP@MOL is enhanced by isolating TBP SSs on the SBUs of the MOL, which prevents aggregation-induced quenching of the excited sensitizers, and by triplet-triplet Dexter energy transfer between excited iridium-based linkers and TBP SSs, which more efficiently harnesses broad-spectrum sonoluminescence. Anchoring TBP on the MOL surface also enhances the energy transfer between the excited sensitizer and ground-state triplet oxygen to increase 1 O2 generation efficacy. In mouse models of colorectal and breast cancer, TBP@MOL demonstrates significantly higher SDT efficacy than Hf-TBP and TBP. This work uncovers a new strategy to design effective nano-sonosensitizers by facilitating energy transfer to efficiently capture broad-spectrum sonoluminescence and enhance 1 O2 generation.

Can financial stress be anticipated and explained? Uncovering the hidden pattern using EEMD-LSTM, EEMD-prophet, and XAI methodologies.

Global financial stress is a critical variable that reflects the ongoing state of several key macroeconomic indicators and financial markets. Predictive analytics of financial stress, nevertheless, has seen very little focus in literature as of now. Futuristic movements of stress in markets can be anticipated if the same can be predicted with a satisfactory level of precision. The current research resorts to two granular hybrid predictive frameworks to discover the inherent pattern of financial stress across several critical variables and geography. The predictive structure utilizes the Ensemble Empirical Mode Decomposition (EEMD) for granular time series decomposition. The Long Short-Term Memory Network (LSTM) and Facebook's Prophet algorithms are invoked on top of the decomposed components to scrupulously investigate the predictability of final stress variables regulated by the Office of Financial Research (OFR). A rigorous feature screening using the Boruta methodology has been utilized too. The findings of predictive exercises reveal that financial stress across assets and continents can be predicted accurately in short and long-run horizons even at the time of steep financial distress during the COVID-19 pandemic. The frameworks appear to be statistically significant at the expense of model interpretation. To resolve the issue, dedicated Explainable Artificial Intelligence (XAI) methods have been used to interpret the same. The immediate past information of financial stress indicators largely explains patterns in the long run, while short-run fluctuations can be tracked by closely monitoring several technical indicators.

A residual driven ensemble machine learning approach for forecasting natural gas prices: analyses for pre-and during-COVID-19 phases.

The natural gas price is an essential financial variable that needs periodic modeling and predictive analysis for many practical implications. Macroeconomic euphoria and external uncertainty make its evolutionary patterns highly complex. We propose a two-stage granular framework to perform predictive analysis of the natural gas futures for the USA (NGF-USA) and the UK natural gas futures for the EU (NGF-UK) for pre-and during COVID-19 phases. The residuals of the previous stage are introduced as a new explanatory feature along with standard technical indicators to perform predictive tasks. The importance of the new feature is explained through the Boruta feature evaluation methodology. Maximal Overlap Discrete Wavelet Transformation (MODWT) is applied to decompose the original time-series observations of the natural gas prices to enable granular level forecasting. Random Forest is invoked on each component to fetch the respective predictions. The aggregated component-wise sums lead to final predictions. A rigorous performance assessment signifies the efficacy of the proposed framework. The results show the effectiveness of the residual as a feature in deriving accurate forecasts. The framework is highly efficient in analyzing patterns in the presence of a limited number of data points during the uncertain COVID-19 phase covering the first and second waves of the pandemic. Our findings reveal that the prediction accuracy is the best for the NGF-UK in the pre-COVID-19 period. Also, the prediction accuracy of the NGF-USA is better in the COVID-19 period than the pre-COVID-19 period.

COVID-19 news and the US equity market interactions: An inspection through econometric and machine learning lens.

This study investigates the impact of COVID-19 on the US equity market during the first wave of Coronavirus using a wide range of econometric and machine learning approaches. To this end, we use both daily data related to the US equity market sectors and data about the COVID-19 news over January 1, 2020-March 20, 2020. Accordingly, we show that at an early stage of the outbreak, global COVID-19s fears have impacted the US equity market even differently across sectors. Further, we also find that, as the pandemic gradually intensified its footprint in the US, local fears manifested by daily infections emerged more powerfully compared to its global counterpart in impairing the short-term dynamics of US equity markets.