Unique presentation of occipital condyle giant cell tumor as occipital condyle syndrome - A review.

Giant cell tumors (GCTs) of the skull are rare and only a few case series with limited number of cases have been reported till date. In the cranium, GCT usually occurs in the sphenoid and temporal bone, occipital condyle GCTs are very rare. We report a rare presentation of GCT of the occipital condyle manifested as occipital condyle syndrome. Despite gross total resection, they can recur aggressively; the presence of cortical breach might be an indicator of aggressiveness prompting early post-operative imaging and adjuvant therapy.

Spectrum, Outcomes, and Mortality Predictors of Acute Kidney Injury among Non-COVID-19 Patients during COVID-19 Pandemic: Data from Four Intensive Care Units.

Introduction: The data of acute kidney injury (AKI), that is, community-acquired AKI (CA-AKI) and hospital-acquired AKI (HA-AKI) among non-COVID patients from intensive care units (ICU) during the coronavirus disease-2019 (COVID-19) pandemic are scarce. We planned to study the change in the profile of such patients compared to the pre-pandemic era. Materials and methods: This prospective observational study was conducted at four ICUs dealing with non-COVID patients at a government hospital in North India, and was aimed at assessing outcomes, and mortality predictors of AKI among non-COVID patients during the COVID-19 pandemic. Renal and patient survival at ICU transfer-out and hospital discharge, ICU and hospital stay duration, mortality predictors, and dialysis requirement at discharge were evaluated. The current or previous COVID-19 infection, previous AKI or chronic kidney disease (CKD), organ donors, and organ transplant patients were excluded. Results: Among the 200 non-COVID-19 AKI patients, diabetes mellitus (DM), primary hypertension, and cardiovascular diseases were the predominant comorbidities in descending order. The commonest cause of AKI was severe sepsis, followed by systemic infections and post-surgery patients. Dialysis requirements at ICU admission during ICU stay and above 30 days were seen in 20.5, 47.5, and 6.5% of patients, respectively. Incidence of CA-AKI and HA-AKI was 1.24:1, whereas dialysis requirement above 30 days was 0.85:1, respectively. The 30-day mortality was 42%. Hepatic dysfunction [hazard ratio (HR): 3.471], septicemia (HR: 3.342), age above 60 years (HR: 4.000), higher sequential organ failure assessment (SOFA) score (HR: 1.107; p = 0.001), anemia (p = 0.003), and low serum iron (p = 0.001) were important mortality predictors in AKI. Conclusion: Compared to the pre-COVID era, CA-AKI was more common than HA-AKI due to restricted elective surgeries during the COVID-19 pandemic. Acute kidney injury with multiorgan involvement and hepatic dysfunction, elderly age with higher SOFA score and sepsis were predictors of adverse renal and patient outcomes. How to cite this article: Singh B, Dogra PM, Sood V, Singh V, Katyal A, Dhawan M, et al. Spectrum, Outcomes, and Mortality Predictors of Acute Kidney Injury among Non-COVID-19 Patients during COVID-19 Pandemic: Data from Four Intensive Care Units. Indian J Crit Care Med 2023;27(2):119-126.

Death registration coverage 2019-2021, India.

Objective: To investigate coverage and factors associated with death registration in India. Methods: We used data from the Indian National Family Health Survey 2019-2021. Based on responses of eligible household members, we estimated death registration in 84 390 deaths in all age groups across the country. We used multilevel logistic regression analysis to determine sociodemographic variables associated with death registration at state, district and individual levels. Findings: Nationally, 70.8% (59 748/84 390) of deaths were registered. Of 707 districts in our study period, 122 and 53 districts had death registration levels less than 40% in females and males, respectively. The likelihood of death registration was significantly lower for females than males (adjusted odds ratios, aOR: 0.61; 95% confidence interval, CI: 0.59-0.64). Death registration increased significantly with age of the deceased person, with the highest odds in 35-49-year-olds (aOR: 5.05; 95% CI: 4.58-5.57) compared with 0-4-year-olds. Death registration was less likely among rural households, disadvantaged castes, the poorest wealth quintile, Muslims and households without a below poverty level card. Higher education was associated with higher death registration with the greatest likelihood of registration in households with a member with post-secondary school education (aOR: 1.54; 95% CI: 1.42-1.66). District-level factors were not significantly associated with death registration. Conclusion: Sociodemographic characteristics of the deceased person were significantly associated with death registration. Strategies to raise awareness of death registration procedures among disadvantaged population groups and the introduction of a mobile telephone application for death registration are recommended to improve death registration in India.

Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams.

The ability of machine learning (ML) techniques to forecast the shear strength of corroded reinforced concrete beams (CRCBs) is examined in the present study. These ML techniques include artificial neural networks (ANN), adaptive-neuro fuzzy inference systems (ANFIS), decision tree (DT) and extreme gradient boosting (XGBoost). A thorough databank with 140 data points about the shear capacity of CRCBs with various degrees of corrosion was compiled after a review of the literature. The inputs parameters of the implemented models are the width of the beam, the effective depth of the beam, concrete compressive strength (CS), yield strength of reinforcement, percentage of longitudinal reinforcement, percentage of transversal reinforcement (stirrups), yield strength of stirrups, stirrups spacing, shear span-to-depth ratio (a/d), corrosion degree of main reinforcement, and corrosion degree of stirrups. The coefficient of determination of the ANN, ANFIS, DT, and XGBoost models are 0.9811, 0.9866, 0.9799, and 0.9998, respectively. The MAPE of the XGBoost model is 99.39%, 99.16%, and 99.28% lower than ANN, ANFIS, and DT models. According to the results of the sensitivity examination, the shear strength of the CRCBs is most affected by the depth of the beam, stirrups spacing, and the a/d. The graphical displays of the Taylor graph, violin plot, and multi-histogram plot additionally support the XGBoost model's dependability and precision. In addition, this model demonstrated good experimental data fit when compared to other analytical and ML models. Accurate prediction of shear strength using the XGBoost approach confirmed that this approach is capable of handling a wide range of data and can be used as a model to predict shear strength with higher accuracy. The effectiveness of the developed XGBoost model is higher than the existing models in terms of precision, economic considerations, and safety, as indicated by the comparative study.

Rodent Model Preclinical Assessment of PEGylated Block Copolymer Targeting Cognition and Oxidative Stress Insults of Alzheimer's Disease.

Misfolded peptide amyloid beta (Aß42), neurofibrillary tangles of hyper-phosphorylated tau, oxidative damage to the brain, and neuroinflammation are distinguished determinants of Alzheimer's disease (AD) responsible for disease progression. This multifaceted neurodegenerative disease is challenging to cure under a single treatment regime until the key disease determinants are traced for their sequential occurrence in disease progression. In an early report, a novel side-chain tripeptide containing PEGylated block copolymer has been tested thoroughly in vitro and in silico for the early inhibition of Aß42 aggregation as well as degradation of preformed Aß42 fibril deposits. The present study demonstrates a preclinical assessment of the PEGylated block copolymer in colchicine-induced AD-mimicking rodent model. The colchicine-induced Wistar rats receiving an intranasal delivery of the block copolymer at a daily dosage of 100 µg/kg and 200 µg/kg body weights, respectively, for 14 days manifested a notable attenuation of behavioral deficit pattern, oxidative stress, and neurotransmitters' deficiency as compared to the untreated ones. The current study also reports the ameliorative property of the PEGylated compound for progressive neuroinflammation and decreased mitochondrial bioenergetics in astrocytoma cell line, viz., U87. A closer look into the drug mechanism of action of a compact 3D PEGylated block copolymer confirmed its disintegrative interaction with Aß42 fibril via in silico simulation. The results obtained from this study signify the potential of the novel PEGylated block copolymer to ameliorate the cognitive decline and progressive oxidative insults in AD and may envision a successful clinical phase trial. The amelioration of disease condition of colchicine-induced AD rat. Initially the rat has given colchicine via stereotaxic surgery which led to a mimicking condition of AD including neuronal death in hippocampal CA1 region. After recovery from the surgery, the rat was treated with the PEGylated block copolymer through intranasal delivery, and this has led to the decrease in neuronal death in hippocampal CA1 region. The mechanism of drug action has shown by the separation of monomer chains of Aß42.

Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN.

The emerging novel variants and re-merging old variants of SARS-CoV-2 make it critical to study the transmission probability in mixed-mode ventilated office environments. Artificial neural network (ANN) and curve fitting (CF) models were created to forecast the R-Event. The R-Event is defined as the anticipated number of new infections that develop in particular events occurring over the course of time in any defined space. In the spring and summer of 2022, real-time data for an office environment were collected in India in a mixed-mode ventilated office space in a composite climate. The performances of the proposed CF and ANN models were compared with respect to traditional statistical indicators, such as the correlation coefficient, RMSE, MAE, MAPE, NS index, and a20-index, in order to determine the merit of the two approaches. Thirteen input features, namely the indoor temperature (TIn), indoor relative humidity (RHIn), area of opening (AO), number of occupants (O), area per person (AP), volume per person (VP), CO2 concentration (CO2), air quality index (AQI), outer wind speed (WS), outdoor temperature (TOut), outdoor humidity (RHOut), fan air speed (FS), and air conditioning (AC), were selected to forecast the R-Event as the target. The main objective was to determine the relationship between the CO2 level and R-Event, ultimately producing a model for forecasting infections in office building environments. The correlation coefficients for the CF and ANN models in this case study were 0.7439 and 0.9999, respectively. This demonstrates that the ANN model is more accurate in R-Event prediction than the curve fitting model. The results show that the proposed ANN model is reliable and significantly accurate in forecasting the R-Event values for mixed-mode ventilated offices.

Enhancing Sustainability of Corroded RC Structures: Estimating Steel-to-Concrete Bond Strength with ANN and SVM Algorithms.

The bond strength between concrete and corroded steel reinforcement bar is one of the main responsible factors that affect the ultimate load-carrying capacity of reinforced concrete (RC) structures. Therefore, the prediction of accurate bond strength has become an important parameter for the safety measurements of RC structures. However, the analytical models are not enough to estimate the bond strength, as they are built using various assumptions and limited datasets. The machine learning (ML) techniques named artificial neural network (ANN) and support vector machine (SVM) have been used to estimate the bond strength between concrete and corroded steel reinforcement bar. The considered input parameters in this research are the surface area of the specimen, concrete cover, type of reinforcement bars, yield strength of reinforcement bars, concrete compressive strength, diameter of reinforcement bars, bond length, water/cement ratio, and corrosion level of reinforcement bars. These parameters were used to build the ANN and SVM models. The reliability of the developed ANN and SVM models have been compared with twenty analytical models. Moreover, the analyzed results revealed that the precision and efficiency of the ANN and SVM models are higher compared with the analytical models. The radar plot and Taylor diagrams have also been utilized to show the graphical representation of the best-fitted model. The proposed ANN model has the best precision and reliability compared with the SVM model, with a correlation coefficient of 0.99, mean absolute error of 1.091 MPa, and root mean square error of 1.495 MPa. Researchers and designers can apply the developed ANN model to precisely estimate the steel-to-concrete bond strength.

The Effect of 10 Crop Plants That Served as Hosts on the Primary Metabolic Profile of the Parasitic Plant Phelipanche aegyptiaca.

Phelipanche aegyptiaca Pers. is a holoparasitic plant that parasitizes various types of host plants. Its penetration into host roots causes a massive reduction in the yield of many crop plants worldwide. The nature of the compounds taken by the parasite from its host is still under debate in the scientific literature. To gain more knowledge about the effect of the hosts on the parasite's primary metabolic profile, GC-MS analyses were conducted on the parasites that developed on 10 hosts from four plant families. There are three hosts from each family: Brassicaceae, Apiaceae and Solanaceae and one host from Fabaceae. The results showed significant differences in the metabolic profiles of P. aegyptiaca collected from the different hosts, indicating that the parasites rely strongly on the host's metabolites. Generally, we found that the parasites that developed on Brassicaceae and Fabaceae accumulated more amino acids than those developed on Apiaceae and Solanaceae that accumulated more sugars and organic acids. The contents of amino acids correlated positively with the total soluble proteins. However, the aromatic amino acid, tyrosine, correlated negatively with the accumulation of the total phenolic compounds. This study contributes to our knowledge of the metabolic relationship between host and parasite.

Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN.

The emerging novel variants and re-merging old variants of SARS-CoV-2 make it critical to study the transmission probability in mixed-mode ventilated office environments. Artificial neural network (ANN) and curve fitting (CF) models were created to forecast the R-Event. The R-Event is defined as the anticipated number of new infections that develop in particular events occurring over the course of time in any defined space. In the spring and summer of 2022, real-time data for an office environment were collected in India in a mixed-mode ventilated office space in a composite climate. The performances of the proposed CF and ANN models were compared with respect to traditional statistical indicators, such as the correlation coefficient, RMSE, MAE, MAPE, NS index, and a20-index, in order to determine the merit of the two approaches. Thirteen input features, namely the indoor temperature (TIn), indoor relative humidity (RHIn), area of opening (AO), number of occupants (O), area per person (AP), volume per person (VP), CO2 concentration (CO2), air quality index (AQI), outer wind speed (WS), outdoor temperature (TOut), outdoor humidity (RHOut), fan air speed (FS), and air conditioning (AC), were selected to forecast the R-Event as the target. The main objective was to determine the relationship between the CO2 level and R-Event, ultimately producing a model for forecasting infections in office building environments. The correlation coefficients for the CF and ANN models in this case study were 0.7439 and 0.9999, respectively. This demonstrates that the ANN model is more accurate in R-Event prediction than the curve fitting model. The results show that the proposed ANN model is reliable and significantly accurate in forecasting the R-Event values for mixed-mode ventilated offices.

Newly designed acrylamide derivative-based pH-responsive hydrogel-urease bioconjugates: synthesis and catalytic urea hydrolysis.

Jack bean urease, the first nickel metalloenzyme, and crystallized enzymes have historical significance due to their several applications in the biomedical and other fields. For the first time, cross-linker free pH-responsive hydrogel-urease bioconjugates have been reported. Without the use of divinyl benzene or divinyl acrylamide derivatives, urease was immobilized inside the hydrogel matrix and various grades of bioconjugates were synthesized. The hydrogel-urease bioconjugate exhibits excellent swelling-deswelling and pH-responsive characteristics without affecting the urease enzyme. The pH-responsive bioconjugates were characterized by FT-IR, powder XRD, SEM, TGA, and UV-vis spectroscopy. Urea hydrolysis and enzyme affinity have been investigated at pH 4, pH 7, and pH 11 using bioconjugates and free urease. At basic pH, BCs showed excellent enzyme activity. In summary, this technique is effective for stabilizing biomacromolecules at different pHs for a variety of real applications.

Description of Photodegradation Mechanisms and Structural Characteristics in Carbon@Titania Yolk-Shell Nanostructures by XAS.

Carbon@titania yolk-shell nanostructures are successfully synthesized at different calcination conditions. These unique structure nanomaterials can be used as a photocatalyst to degrade the emerging water pollutant, acetaminophen (paracetamol). The photodegradation analysis studies have shown that the samples with residual carbon nanospheres have improved the photocatalytic efficiency. The local electronic and atomic structure of the nanostructures are analyzed by X-ray absorption spectroscopy (XAS) measurements. The spectra confirm that the hollow shell has an anatase phase structure, slight lattice distortion, and variation in Ti 3d orbital orientation. In situ XAS measurements reveal that the existence of amorphous carbon nanospheres inside the nano spherical shell inhibit the recombination of electron-hole pairs; more mobile holes are formed in the p-d hybridized bands near the Fermi surface and enables the acceleration of the carries that significantly enhance the photodegradation of paracetamol under UV-visible irradiation. The observed charge transfer process from TiO2  hybridized orbital to the carbon nanospheres reduces the recombination rate of electrons and holes, thus increasing the photocatalytic efficiency.

USP7 targets XIAP for cancer progression: Establishment of a p53-independent therapeutic avenue for glioma.

Ubiquitin specific peptidase 7 (USP7) is a deubiquitinating enzyme (DUB) that removes ubiquitin tags from specific target protein substrates in order to alter their degradation rate, sub-cellular localization, interaction, and activity. The induction of apoptosis upon USP7 inhibition is well established in cancer containing wild type p53, which operates through the 'USP7-Mdm2-p53' axis. However, in cancers without functional p53, USP7-dependent apoptosis is induced through many other alternative pathways. Here, we have identified another critical p53 independent path active under USP7 to regulate apoptosis. Proteomics analysis identifies XIAP as a potential target of USP7-dependent deubiquitination. GSEA analysis revealed up-regulation of apoptosis signalling upon USP7 inhibition associated with XIAP down-regulation. Modulation of USP7 expression and activity in multiple cancer cell lines showed that USP7 deubiquitinates XIAP to inhibit apoptosis in a caspase-dependent pathway, and the combinatorial inhibition of USP7 and XIAP induces apoptosis in vitro and in vivo. Immunohistochemical staining revealed that grade-wise accumulation of USP7 correlated with an elevated level of XIAP in glioma tissue. This is the first report on the identification and validation of XIAP as a novel substrate of USP7 and together, they involve in the empowerment of the tumorigenic potential of cancer cells by inhibiting apoptosis.

Sub-mucosal Dexamethasone for Post-operative Pain and Oedema Control in Lower Third Molar Surgery.

Background: Surgical excision of the problematic mandibular third molars causes substantial tissue damage and an inflammatory response. Discomfort and edema are common postoperative signs and symptoms caused by the latter. To find out whether dexamethasone may help with the edema and pain that come along with the surgical removal of impacted mandibular third molars, researchers have performed clinical studies. Methods: Twenty individuals with bilaterally affected mandibular third molars who were scheduled for extraction participated in a prospective trial. At two separate sessions, teeth were raised and cut after buccal ostectomy. Since the surgical operation on the left foot, both patients were administered a mixture of 4 mg dexamethasone submucosal injection and antibiotics for 3 days. On the 1st, 3rd, and 7th postoperative days, edema and pain were assessed. Results: At the 1st, 3rd, and 7th postoperative days, there was a clinically meaningful decrease in the level of edema and discomfort in both arms. Conclusions: The current report offers empirical proof that administering a 4 mg dexamethasone submucosal injection during surgery greatly reduced post-surgical edema and discomfort.

The Use of Computational Geometry Techniques to Resolve the Issues of Coverage and Connectivity in Wireless Sensor Networks.

Wireless Sensor Networks (WSNs) enhance the ability to sense and control the physical environment in various applications. The functionality of WSNs depends on various aspects like the localization of nodes, the strategies of node deployment, and a lifetime of nodes and routing techniques, etc. Coverage is an essential part of WSNs wherein the targeted area is covered by at least one node. Computational Geometry (CG) -based techniques significantly improve the coverage and connectivity of WSNs. This paper is a step towards employing some of the popular techniques in WSNs in a productive manner. Furthermore, this paper attempts to survey the existing research conducted using Computational Geometry-based methods in WSNs. In order to address coverage and connectivity issues in WSNs, the use of the Voronoi Diagram, Delaunay Triangulation, Voronoi Tessellation, and the Convex Hull have played a prominent role. Finally, the paper concludes by discussing various research challenges and proposed solutions using Computational Geometry-based techniques.

Identification of Cardiac Patients Based on the Medical Conditions Using Machine Learning Models.

Chronic diseases are the most severe health concern today, and heart disease is one of them. Coronary artery disease (CAD) affects blood flow to the heart, and it is the most common type of heart disease which causes a heart attack. High blood pressure, high cholesterol, and smoking significantly increase the risk of heart disease. To estimate the risk of heart disease is a complex process because it depends on various input parameters. The linear and analytical models failed due to their assumptions and limited dataset. The existing studies have used medical data for classification purposes, which help to identify the exact condition of the patient, but no one has developed any correlation equation which can be directly used to identify the patients. In this paper, mathematical models have been developed using the medical database of patients suffering from heart disease. Curve fitting and artificial neural network (ANN) have been applied to model the condition of patients to find out whether the patient is suffering from heart disease or not. The developed curve fitting model can identify the cardiac patient with accuracy, having a coefficient of determination (R 2-value) of 0.6337 and mean absolute error (MAE) of 0.293 at a root mean square error (RMSE) of 0.3688, and the ANN-based model can identify the cardiac patient with accuracy having a coefficient of determination (R 2-value) of 0.8491 and MAE of 0.20 at RMSE of 0.267, it has been found that ANN provides superior mathematical modeling than curve fitting method in identifying the heart disease patients. Medical professionals can utilize this model to identify heart patients without any angiography or computed tomography angiography test.

Towards Non-stick Silk: Tuning the Hydrophobicity of Silk Fibroin Protein.

Silk fibroin protein is a biomaterial with excellent biocompatibility and low immunogenicity. These properties have catapulted the material as a leader for extensive use in stents, catheters, and wound dressings. Modulation of hydrophobicity of silk fibroin protein to further expand the scope and utility however has been elusive. We report that installing perfluorocarbon chains on the surface of silk fibroin transforms this water-soluble protein into a remarkably hydrophobic polymer that can be solvent-cast. A clear relationship emerged between fluorine content of the modified silk and film hydrophobicity. Water contact angles of the most decorated silk fibroin protein exceeded that of Teflon®. We further show that water uptake in prefabricated silk bars is dramatically reduced, extending their lifetimes, and maintaining mechanical integrity. These results highlight the power of chemistry under moderate conditions to install unnatural groups onto the silk fibroin surface and will enable further exploration into applications of this versatile biomaterial.

Cerebellopontine Angle Epidermoids: Comparative Results of Microscopic and Endoscopic Excision Using the Retromastoid Approach".

Background Densely packed neurovascular structures, often times inseparable capsular adhesions and sometimes a multicompartmental tumor extension, make surgical excision of cerebellopontine angle epidermoids (CPEs) a challenging task. A simultaneous or an exclusive endoscopic visualization has added a new dimension to the classical microscopic approaches to these tumors recently. Methods Eighty-six patients (age: 31.6 ± 11.7 years, M:F = 1:1) were included. Nineteen patients (22.1%) had a multicompartmental tumor. Tumor extension was classified into five subtypes. Sixty-two patients underwent a pure microscopic approach (72%) out of which 10 patients (16%) underwent an endoscope-assisted surgery (11.6%) and 24 patients (28%) underwent an endoscope-controlled excision. Surgical outcomes were retrospectively analyzed. Results Headache (53.4%), hearing loss (46.5%), and trigeminal neuralgia (41.8%) were the leading symptoms. Interestingly, 21% of the patients had at least one preexisting cranial nerve deficit. Endoscopic assistance helped in removing an unseen tumor lobule in 3 of 10 patients (30%). Pure endoscopic approach significantly reduced the hospital stay from 9.2 to 7.3 days ( p = 0.012), and had a statistically insignificant yet a clearly noticeable lesser incidence of subtotal tumor excision (0 vs. 10%, p = 0.18) with comparable cranial nerve deficits but with a higher postoperative cerebrospinal fluid (CSF) leak rate (29% vs. 4.8%, p = 0.004). Conclusion Endoscope assistance in CPE surgery is a useful addition to conventional microscopic retromastoid approach. Pure endoscopic excision in CPE is feasible, associated with a lesser duration of hospital stay, better extent of excision in selected cases, and it has a comparable cranial nerve morbidity profile albeit with a higher rate of CSF leak.

Material Properties, Dissolution and Time Evolution of PEGylated Lipid-Shelled Microbubbles: Effects of the Polyethylene Glycol Hydrophilic Chain Configurations.

Polyethylene glycol (PEG) is often added to the lipid coating of a contrast microbubble to prevent coalescence and improve circulation. At high surface density, PEG chains are known to undergo a transition from a mushroom configuration to an extended brush configuration. We investigated the effects of PEG chain configuration on attenuation and dissolution of microbubbles by varying the molar ratio of the PEGylated lipid in the shell with three (0%, 2% and 5%) in the mushroom configuration and two (10% and 20%) in the brush configuration. We measured attenuation through the bubble suspensions and used it to obtain the characteristic rheological properties of their shells according to two interfacial rheological models. The interfacial elasticity was found to be significantly lower in the brush regime (∼0.6 N/m) than in the mushroom regime (∼1.3 N/m), but similar in value within each regime. The dissolution behavior of microbubbles under acoustic excitation inside an air-saturated medium was studied by measuring the time-dependent attenuation. Total attenuation recorded a transient increase because of growth resulting from air influx and an eventual decrease caused by dissolution. Microbubble shell composition with varying PEG concentrations had significant effects on dissolution dynamics.

Performance barriers of Civil Registration System in Bihar: An exploratory study.

OBJECTIVES: Vital statistics generated by the Civil Registration System (CRS) are essential for developing healthcare interventions at all administrative levels. Bihar had one of the lowest levels of mortality registration among India's states. This study investigates CRS's performance barriers from the perspective of CRS staff and community members in Bihar. METHODS: We conducted a primary qualitative survey in the two districts of Bihar during February-March 2020 with CRS staff (n = 15) and community members (n = 90). We purposively selected the Patna and Vaishali districts of Bihar for the survey. Thematic analysis was done to identify the pattern across the data using the Atlas-ti software. RESULTS: Most participants showed a good understanding of registration procedures and birth and death registration benefits. The perceived need for death registration is lower than birth registration. Birth registration was higher among female children than male children. We found that most participants did not report children or adult female death due to lack of financial or property-related benefits. Most participants faced challenges in reporting birth and death due to poor delivery of services at the registration centres, higher indirect opportunity cost, and demand of bribes by the CRS staff for providing certificates. We found a lack of adequate investment, shortage of dedicated staff, and limited computer and internet services at the registration centres. CONCLUSIONS: Poor data on birth and death registration could lead decision-makers to target health services inappropriately. Strengthening health institutions' linkage with the registration centres, mobile registration in far-flung areas and regular CRS staff training could increase death registration levels. An adequate awareness campaign on the benefits of birth and death registration is required to increase the reporting of vital events.