Mycobacterial biofilms: Understanding the genetic factors playing significant role in pathogenesis, resistance and diagnosis.

Even though the genus Mycobacterium is a diverse group consisting of a majority of environmental bacteria known as non-tuberculous mycobacteria (NTM), it also contains some of the deadliest pathogens (Mycobacterium tuberculosis) in history associated with chronic disease called tuberculosis (TB). Formation of biofilm is one of the unique strategies employed by mycobacteria to enhance their ability to survive in hostile conditions. Biofilm formation by Mycobacterium species is an emerging area of research with significant implications for understanding its pathogenesis and treatment of related infections, specifically TB. This review provides an overview of the biofilm-forming abilities of different species of Mycobacterium and the genetic factors influencing biofilm formation with a detailed focus on M. tuberculosis. Biofilm-mediated resistance is a significant challenge as it can limit antibiotic penetration and promote the survival of dormant mycobacterial cells. Key genetic factors promoting biofilm formation have been explored such as the mmpL genes involved in lipid transport and cell wall integrity as well as the groEL gene essential for mature biofilm formation. Additionally, biofilm-mediated antibiotic resistance and pathogenesis highlighting the specific niches, sites of infection along with the possible mechanisms of biofilm dissemination have been discussed. Furthermore, drug targets within mycobacterial biofilm and their role as potential biomarkers in the development of rapid diagnostic tools have been highlighted. The review summarises the current understanding of the complex nature of Mycobacterium biofilm and its clinical implications, paving the way for advancements in the field of disease diagnosis, management and treatment against its multi-drug resistant species.

Facile green synthesis of silver nanoparticles derived from the medicinal plant Clerodendrum serratum and its biological activity against Mycobacterium species.

The emergence of multidrug-resistant mycobacterial strains is a significant crisis that has led to higher treatment failure rates and more toxic and expensive medications for tuberculosis (TB). The urgent need to develop novel therapeutics has galvanized research interest towards developing alternative antimicrobials such as silver nanoparticles (AgNPs). The current study focused on the anti-mycobacterial activity of green-synthesized AgNPs and its polyethylene glycol encapsulated derivative (PEG-AgNPs) with improved stability using the leaves extract of Clerodendrum serratum. Different characterization methods were used to analyze them. DLS analysis revealed a lower polydispersity index of PEG-AgNPs, suggesting a more uniform size distribution than that of AgNPs. The HR-TEM results revealed that the AgNPs and PEG-AgNPs have predominantly spherical shapes in the size range of 9-35 nm and 15-60 nm, respectively, while positive values of Zeta potential indicate their stability. FTIR-ATR analysis confirmed the presence of functional groups responsible for reducing and capping the bio-reduced AgNPs, whereas the XRD data established its crystalline nature. Impressively, the PEG-AgNPs exhibited maximum inhibitory activity against different Tubercular and Non-Tuberculous Mycobacterium species i.e., Mycobacterium smegmatis, Mycobacterium fortuitum and Mycobacterium marinum, relative to those of AgNPs and Linezolid. The flow cytometry assay showed that the anti-mycobacterial action was mediated by an increase in cell wall permeability. Notably, the results of AFM confirm their ability to inhibit mycobacterial biofilm significantly. We demonstrated the nontoxic nature of these AgNPs, explicated by the absence of hemolytic activity against human RBCs. Overall, the results suggest that PEG-AgNPs could offer a novel therapeutic approach with potential anti-mycobacterial activity and can overcome the limitations of existing TB therapies.

Aerosol-PM2.5 Dynamics: In-situ and satellite observations under the influence of regional crop residue burning in post-monsoon over Delhi-NCR, India.

The increasing air pollution in the urban atmosphere is adversely impacts the environment, climate and human health. The alarming degradation of air quality, atmospheric conditions, economy and human life due to air pollution needs significant in-depth studies to ascertain causes, contributions and impacts for developing and implementing an effective policy to combat these issues. This work lies in its multifaceted approach towards comprehensive understanding and mitigating severe pollution episodes in Delhi and its surrounding areas. We investigated the aerosol dynamics in the post-monsoon season (PMS) from 2019 to 2022 under the influence of both crop residue burning and meteorological conditions. The study involves a broad spectrum of factors, including PM2.5 concentrations, active fire events, and meteorological parameters, shedding light on previously unexplored studies. The average AOD550 (0.79) and PM2.5 concentration (140.12 µg/m³) were the highest in 2019. PM2.5 was higher from mid-October to mid-November each year, exceeding the WHO guideline of 15 µg/m³ (24 h) by 27-34 times, signifying a public health emergency. A moderate to strong correlation between PM2.5 and AOD was found (r = 0.65) in 2021. The hotspot region accounts for almost 50% (2019), 47.51% (2020), 57.91% (2021) and 36.61% (2022) of the total fire events. A statistically significant negative non-linear correlation (r) was observed between wind speed (WS) and both AOD and PM2.5 concentration, influencing air quality over the region. HYSPLIT model and Windrose result show the movement of air masses predominated from the North and North-West direction during PMS. This study suggest to promotes strategies such as alternative waste management, encouraging modern agricultural practices in hot-spot regions, and enforcing strict emission norms for industries and vehicles to reducing air pollution and its detrimental effects on public health in the region and also highlights the need for future possibilities of research to attract the global attention.

Evaluation of Surface Water Quality in the Betwa River Basin through the Water Quality Index Model and Multivariate Statistical Techniques.

The Betwa River Basin (BRB), a sub-basin of the River Yamuna, is the oldest flowing water system in Central India. The water quality of the rivers are under stress, hence regular monitoring and appraisal is required to know the health of the rivers. Factor analysis and principal component analysis (FA/PCA) multivariate statistical techniques were used to extract three and four varimax factors that explained 96.408 and 100.00 percent of the total variance in water quality, respectively. Cluster analysis (CA) categorizes observed items into distinct quality categories based on correlations between stations and years. Point industrial/sewage effluents, diffuse pollution as runoff from arable land, erosion, and natural source pollution contribute to the pollution of the BRB. As a result, water quality is threatened or impaired, and conditions often departed from natural or desirable levels at Rajghat, Garrauli, Mohana, and Shahijina stations. According to the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI), the surface water quality at the Rajghat and Mohana stations corresponds to fair ecological status. However, the surface water quality of the Garrauli and Shahijina stations has a marginal water quality as per CCME-WQI. From 1985 to 2018, the Shahijina had the most considerable load of nutrients and organic matter, as determined by the CCME-WQI and by comparing the water quality data. A thorough examination had revealed a fluctuating trend in the BRB pollution, particularly at all stations. Results indicate that between 1985 and 2018, the only defense mechanism of the river was the auto purification mechanism, which is strongly influenced by the drought, point pollution source, and extreme meteorological events that probably cause these fluctuations.

Biophysical and in-silico studies on the structure-function relationship of Brugia malayi protein disulfide isomerase.

Human Lymphatic filariasis is caused by parasitic nematodes Wuchereria bancrofti, Brugia malayi, and Brugia timori. Protein disulfide isomerase (PDI), a redox-active enzyme, helps to form and isomerize the disulfide bonds, thereby acting as a chaperone. Such activity is essential for activating many essential enzymes and functional proteins. Brugia malayi protein disulfide isomerase (BmPDI) is crucial for parasite survival and an important drug target. Here, we used a combination of spectroscopic and computational analysis to study the structural and functional changes in the BmPDI during unfolding. Tryptophan fluorescence data revealed two well-separated transitions during the unfolding process, suggesting that the unfolding of the BmPDI is non-cooperative. The binding of the fluorescence probe 8-anilino-1-naphthalene sulfonic acid dye (ANS) validated the results obtained by the pH unfolding. The dynamics of molecular simulation performed at different pH conditions revealed the structural basis of BmPDI unfolding. Detailed analysis suggested that under different pH, both the global structure and the conformational dynamics of the active site residues were differentially altered. Our multiparametric study reveals the differential dynamics and collective motions of BmPDI unfolding, providing insights into its structure-function relationship.Communicated by Ramaswamy H. Sarma.

Land cover and crop types mapping using different spatial resolution imagery in a Mediterranean irrigated area.

Crop type identification is critical for agricultural sustainability policy development and environmental assessments. Therefore, it is important to obtain their spatial distribution via different approaches. Medium-, high- and very high-resolution optical satellite sensors are efficient tools for acquiring this information, particularly for challenging studies such as those conducted in heterogeneous agricultural fields. This research examined the ability of four multitemporal datasets (Sentinel-1-SAR (S1), Sentinel-2-MSI (S2), RapidEye (RE), and PlanetScope (PS)) to identify land cover and crop types (LCCT) in a Mediterranean irrigated area. To map LCCT distribution, a supervised pixel-based classification is adopted using Support Vector Machine with a radial basis function kernel (SVMRB) and Random Forest (RF). Thus, LCCT maps were generated into three levels, including six (Level I), ten (Level II), and fourteen (Level III) classes. Overall, the findings revealed high overall accuracies of >92%, >83%, and > 81% for Level I, Level II, and Level III, respectively, except for Sentinel-1. It was found that accuracy improves considerably when the number of classes decreases, especially when cropland or non-cropland classes are grouped into one. Furthermore, there was a similarity in performance between S2 alone and S1S2. PlanetScope LCCT classifications outperform other sensors. In addition, the present study demonstrated that SVM achieved better performances against RF and can thereby effectively extract LCCT information from high-resolution imagery as PlanetScope.

Role of site-site interaction on the phase equilibria of multiple-site associating fluids in a functionalized slit pore.

Vapor-liquid phase equilibria for multiple sites associating fluids with different associating strengths are investigated in a slit pore using grand-canonical transition matrix Monte Carlo method. The increase of critical temperature from two-site to four-site associating fluids at constant site strength is quite significant as compared to that of the one-site to two-site associating fluids, which is more pronounced at higher associating strength (ϵ* = 6). Monomer fraction and cluster size distribution are used to investigate the association of fluid particles in coexistence phases. The monomer fraction for both phases decreases with increased associating sites on the fluid particles due to more site-site interaction with neighboring fluid particles and forming a larger cluster. Therefore, the number of associating sites and their distribution play a vital role in the association of fluid particles. Moreover, the saturation chemical potential changes with the arrangement of the sites. For two-site associating fluids, we observe early vapor-liquid transition when the sites are oppositely placed, and when the sites are placed at 90°, the vapor-liquid transition is observed at the higher chemical potential. Moreover, four-site associating fluids with a square arrangement show early vapor-liquid phase transition, mainly because these arrangements of sites effectively interact with surface sites and the molecules in the next layer.

Future prediction of water balance using the SWAT and CA-Markov model using INMCM5 climate projections: a case study of the Silwani watershed (Jharkhand), India.

The aim of this research was to simulate the future water balance of the Silwani watershed, Jharkhand, India, under the combined effect of land use and climate change based on the Soil and Water Assessment Tool (SWAT) and Cellular Automata (CA)-Markov Chain model. The future climate prediction was done based on daily bias-corrected datasets of the INMCM5 climate model with Shared Socioeconomic Pathway 585 (SSP585), which represent the fossil fuel development of the world. After a successful model run, water balance components like surface runoff, groundwater contribution to stream flow, and ET were simulated. The anticipated change in land use/land cover (LULC) between 2020 and 2030 reflects a slight increase (3.9 mm) in groundwater contribution to stream flow while slight decrease in surface runoff (4.8 mm). The result of this research work helps the planners to plan any similar watershed for future conservation.

Modeling of land use change under the recent climate projections of CMIP6: a case study of Indian river basin.

The aim of the study was to investigate the land use change dynamics under CMIP6 projections using Land Change Modeler (LCM). The Global Sensitivity Analysis (GSA) techniques was applied to quantify the sensitivity of single parameter and combination of parameters. Land use and land cover (LULC) transitions of the baseline period (2006-2016) was assessed with a model performance accuracy of 80%. Receiver operating characteristic (ROC) analysis shows that the model has performed well for all the LULC classes except builtup land. Prediction under the SSP245 scenario depicts that areal extent of agricultural, forest, and snow, and glacier will decrease by the mid-century (2045). However, the grassland and barren land area will increase from the baseline period. A similar change pattern with a higher magnitude has also been predicted under SSP585 scenario. The CMIP6 forcing index considers socio-economic effects and LCM predicted an expansion in barren land which may be attributed to changes in cryosphere in the studied area.

Integration of hydrogeological data, GIS and AHP techniques applied to delineate groundwater potential zones in sandstone, limestone and shales rocks of the Damoh district, (MP) central India.

The Damoh district, which is located in the central India and characterized by limestone, shales, and sandstone compact rock. The district has been facing groundwater development challenges and problems for several decades. To facilitate groundwater management, it is crucial to monitoring and planning based on geology, slope, relief, land use, geomorphology, and the types of the basaltic aquifer in the drought-groundwater deficit area. Moreover, the majority of farmers in the area are heavily dependent on groundwater for their crops. Therefore, delineation of groundwater potential zones (GPZ) is essential, which is defined based on various thematic layers, including geology, geomorphology, slope, aspect, drainage density, lineament density, topographic wetness index (TWI), topographic ruggedness index (TRI), and land use/land cover (LULC). The processing and analysis of this information were carried out using Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) methods. The validity of the results was trained and tested using Receiver Operating Characteristic (ROC) curves, which showed training and testing accuracies of 0.713 and 0.701, respectively. The GPZ map was classified into five classes such as very high, high, moderate, low, and very low. The study revealed that approximately 45% of the area falls under the moderate GPZ, while only 30% of the region is classified as having a high GPZ. The area receives high rainfall but has very high surface runoff due to no proper developed soil and lack of water conservation structures. Every summer season show a declined groundwater level. In this context, results of study area are useful to maintain the groundwater under climate change and summer season. The GPZ map plays an important role in implementing artificial recharge structures (ARS), such as percolation ponds, tube wells, bore wells, cement nala bunds (CNBs), continuous contour trenching (CCTs), and others for development of ground level. This study is significant for developing sustainable groundwater management policies in semi-arid regions, that are experiencing climate change. Proper groundwater potential mapping and watershed development policies can help mitigate the effects of drought, climate change, and water scarcity, while preserving the ecosystem in the Limestone, Shales, and Sandstone compact rock region. The results of this study are essential for farmers, regional planners, policy-makers, climate change experts, and local governments, enabling them to understand the groundwater development possibilities in the study area.

Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models.

Agriculture, meteorological, and hydrological drought is a natural hazard which affects ecosystems in the central India of Maharashtra state. Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this paper, we have focused on the prediction accuracy of meteorological drought in the semi-arid region based on the standardized precipitation index (SPI) using the random forest (RF), random tree (RT), and Gaussian process regression (GPR-PUK kernel) models. A different combination of machine learning models and variables has been performed for the forecasting of metrological drought based on the SPI-6 and 12 months. Models were developed using monthly rainfall data for the period of 2000-2019 at two meteorological stations, namely, Karanjali and Gangawdi, each representing a geographical region of Upper Godavari river basin area in the central India of Maharashtra state which frequently experiences droughts. Historical data from the SPI from 2000 to 2013 was processed to train the model into machine learning model, and the rest of the 2014 to 2019-year data were used for testing to forecast the SPI and metrological drought. The mean square error (MSE), root mean square error (RMSE), adjusted R2, Mallows' (Cp), Akaike's (AIC), Schwarz's (SBC), and Amemiya's PC were used to identify the best combination input model and best subregression analysis for both stations of SPI-6 and 12. The correlation coefficient ([Formula: see text]), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) were used to perform evaluation for SPI-6 and 12 months of both stations with RF, RT, and GPR-PUK kernel models during the training and testing scenarios. The results during testing phase revealed that the RF was found as the best model in forecasting droughts with values of [Formula: see text], MAE, RMSE, RAE (%), and RRSE (%) being 0.856, 0.551, 0.718, 74.778, and 54.019, respectively, for SPI-6 while 0.961, 0.361, 0.538, 34.926, and 28.262, respectively, for SPI-12 scales at Gangawdi station. Further, the respective values of evaluators at Karanjali station were 0.913 and 0.966, 0.541 and 0.386, 0.604 and 0.589, 52.592 and 36.959, and 42.315 and 31.394 for PUK kernel and RT models, respectively, during SPI-6 and SPI-12. Machine learning models are potential drought warning techniques because they take less time, have fewer inputs, and are less sophisticated than dynamic or scientific models.

Indole: A promising scaffold for the discovery and development of potential anti-tubercular agents.

Indole-containing small molecules have been reported to have diverse pharmacological activities. The aromatic heterocyclic scaffold, which resembles various protein structures, has received attention from organic and medicinal chemists. Exploration of indole derivatives in drug discovery has rapidly yielded a vast array of biologically active compounds with broad therapeutic potential. Nature is the major source of indole scaffolds, but various classical and advanced synthesis methods for indoles have also been reported. One-pot synthesis is widely considered an efficient approach in synthetic organic chemistry and has been used to synthesize some indole compounds. The rapid emergence of drug-resistant tuberculosis is a major challenge to be addressed. Identifying novel targets and drug candidates for tuberculosis is therefore crucial. Researchers have extensively explored indole derivatives as potential anti-tubercular agents or drugs. Indole scaffolds containing the novel non-covalent (decaprenylphosphoryl-ß-D-ribose2'-epimerase) DprE1 inhibitor 1,4-azaindole is currently in clinical trials to treat Mycobacterium tuberculosis. In addition, DG167 indazole sulfonamide with potent anti-tubercular activity is undergoing early-stage development in preclinical studies. Indole bearing cationic amphiphiles with high chemical diversity have been reported to depolarize and disrupt the mycobacterial membrane. Some indole-based compounds have potential inhibitory activities against distinct anti-tubercular targets, including the inhibition of cell wall synthesis, replication, transcription, and translation, as summarized in the graphical abstract. The success of computer-aided drug design in the fields of cancer and anti-viral drugs has accelerated in silico studies in antibacterial drug development. This review describes the sources of indole scaffolds, the potential for novel indole derivatives to serve as anti-tubercular agents, in silico findings, and proposed actions to facilitate the design of novel compounds with anti-tubercular activity.

Remote sensing and in-situ approach for investigation of pelagic communities in the reservoirs of the electrical power complex.

BACKGROUND: Two closely located reservoirs on the Southern Bug River and its tributary in the southern region of Ukraine were compared to study the impact of temperature on hydrobionts and pelagic communities, a major ecologic issue in the climate warming context, using in-situ and satellite remote sensing data. These reservoirs are parts of the South-Ukraine electric power-producing complex. The Tashlyk reservoir is a cooling reservoir for the nuclear power plant, and Oleksandrivske reservoir is used for production of hydroelectricity and irrigation. The cooling reservoir is replenished by pumping water from the upper part of the Oleksandrivske reservoir. METHODS: The relationships of temperature, transparency, and distribution of phytoplankton and zooplankton communities were established based on satellite remote sensing data and in-situ during 2013-2021. The main variables of phytoplankton and zooplankton were compared, and for improved understanding features, spatial distribution maps were created. RESULTS: It was found that the distribution of coenotic groups of phytoplankton and zoonplankton in the cooling reservoir (Tashlyk) corresponds to thermal conditions. Three communities of phytoplankton and two communities of zooplankton were identified in the Tashlyk reservoir. However, in the Oleksandrivske reservoir, separate communities of phytoplankton and zooplankton were reported along its length. CONCLUSIONS: It was shown that both on land and in the Oleksandrivske reservoir, there is an increase in temperature in summer, an increasing trend in the global warming context, but that was not observed in the cooling reservoir of the nuclear power plant (NPP). It let us assume that the factors such as temperature or nutrients impact can be assessed as external significant factors related to the catchment area for the reservoirs with different types of using.

Recurrent retrorectal tailgut cyst mimicking deep pelvic abscess: A diagnostic dilemma.

Tailgut cysts are congenital anomalies that are rare and arise from incompletely regressed primitive hindgut. These are more commonly found in women and are usually asymptomatic. When symptoms develop, these can present with pain, infection, hemorrhage, difficulty in defecation, and rarely malignant change. We report a middle-aged married woman who presented with deep-seated perineal pain for a couple of months, which increased during defecation and sexual intercourse. Although abdominal examination was unremarkable barring deep tenderness in the hypogastrium, rectal and vaginal examinations suggested a tender pelvic swelling. An abdominal ultrasonographic examination diagnosed a cystic swelling in the pelvis extending until the Levator ani muscles. Considering her symptoms, a pelvic abscess was diagnosed and transvaginal drainage was done. Due to persistence of symptoms and recurrence after a month, she was further investigated and was diagnosed to have a presacral benign cystic tumor based on CT and MRI scans of the pelvis. The lesion was completely excised through a combined abdomino-perineal approach and histopathological report suggested a benign tailgut cyst. That a cystic presacral swelling with features of inflammation can be confused with a deep pelvic abscess is hereby highlighted in this report. An MRI scan is diagnostic of these lesions. Failure to differentiate it from a pelvic abscess may result in drainage, which may be of concern if the lesion is malignant.

Evaluation of Bacteriophage Cocktail on Septicemia Caused by Colistin-Resistant Klebsiella pneumoniae in Mice Model.

Objective: The emergence of resistance against last-resort antibiotics, carbapenem and colistin, in Klebsiella pneumoniae has been reported across the globe. Bacteriophage therapy seems to be one of the most promising alternatives. This study aimed to optimize the quantity and frequency of bacteriophage cocktail dosage/s required to eradicate the Klebsiella pneumoniae bacteria in immunocompetent septicemic mice. Methods: The three most active phages ɸKpBHU4, ɸKpBHU7, and ɸKpBHU14 characterized by molecular and TEM analyses were in the form of cocktail and was given intraperitoneally to mice after inducing the septicemia mice model with a constant dose of 8 × 107 colony-forming unit/mouse (CFU/mouse) Klebsiella pneumoniae. After that, the efficacy of the phage cocktail was analyzed at different dosages, that is, in increasing, variable, constant, and repeated dosages. Furthermore, interleukin-6 and endotoxin levels were estimated with variable doses of phage cocktail. Results: We have elucidated that phage therapy is effective against the Klebsiella pneumoniae septicemia mice model and is a promising alternative to antibiotic treatments. Our work delineates that a single dose of phage cocktail with 1 × 105 plaque-forming unit/mouse (PFU/mouse) protects the mice from fatal outcomes at any stage of septicemia. However, a higher phage dosage of 1 × 1012 PFU/mice is fatal when given at the early hours of septicemia, while this high dose is not fatal at the later stages of septicemia. Moreover, multiple repeated dosages are required to eradicate the bacteria from peripheral blood. In addition, the IL-6 levels in the 1 × 105 PFU/mouse group remain lower, but in the 1 × 1012 PFU/mouse group remains high at all points, which were associated with fatal outcomes. Conclusion: Our study showed that the optimized relatively lower and multiple dosages of phage cocktails with the strict monitoring of vitals in clinical settings might cure septicemia caused by MDR bacteria with different severity of infection.

Multi-criteria decision making and Dempster-Shafer model-based delineation of groundwater prospect zones from a semi-arid environment.

The present study illustrates the delineation of the groundwater potential zones in one of the most critical and drought-affected areas under Bundelkhand region of Uttar Pradesh (India). Hydrological evaluations were carried out using GIS tools and remote sensing data which ultimately yielded several thematic maps, such as lineament density, land use/land cover, drainage density, lithology, slope, geomorphology, topographic wetness index (TWI), DEM, and soil. Thematic layers were assigned relative weightages as per their groundwater potential prospects under multi-criteria decision making (MCDM) method through analytical hierarchy process (AHP). To recognize the groundwater potential zone, weighted overlay analysis was also performed. Additionally, for testing of the Dempster-Shafer model, 16 wells in the study area have been selected. Based on the probability of the groundwater occurrence, the belief factor was equated to delineate groundwater potential zones which illustrate five different potential zones. According to the AHP model, the northwest side of the study area is characterized with very high potential zones whereas the northeast and southeast regions constitute medium and low groundwater potential zones respectively. According to the DS model, very high groundwater potential zones constitute 17% and the remaining area falls under low potential. Overall accuracy of the DS model is higher than AHP model.

Extended Total Mesorectal Excision (e-TME) for Locally Advanced Rectal Cancer.

BACKGROUND: Locally advanced rectal cancer (LARC) can involve surrounding pelvic organs requiring multivisceral resection. Extended total mesorectal excision (e-TME) or multivisceral resection is a complex procedure associated with high morbidity, mortality, and R1 resection rates. However, e-TME in LARC with surrounding organ involvement is the only potential option for cure. The study aims to assess the clinical outcome of patients requiring e-TME for LARC. METHODS: The study is a retrospective review of all patients with LARC requiring multivisceral resection (2013 to 2019). The database includes clinic-demographic profile, pelvic organ involved, operative details, resection margin status, morbidity, mortality, and survival. RESULTS: Seven consecutive patients (9.2%) out of 76 LARC (median age 46 years; 5 females) required multivisceral resection. The organs involved were bladder (4); posterior wall of vagina (2); and uterus (1). The en bloc resection included total cystoprostatectomy - 1; partial cystectomy - 3; posterior vaginectomy - 2; and hysterectomy - 1. Additionally, four required abdominoperineal resection. All were adenocarcinoma: stage III, with R0 resection - 76%. The overall complications were seen in 60% of patients, majority being wound related. There was no operative mortality. The median survival was 32.2 months in the entire series, while one died with the disease at a 28-month follow-up. CONCLUSION: e-TME with curative intent, though a complex procedure, is associated with high wound-related morbidity, R1 resection, but improved median survival benefit.

A new method to map groundwater-dependent ecosystem zones in semi-arid environments: A case study in Chile.

Groundwater (GW) use has intensified in recent decades, threatening the ecological integrity of groundwater-dependent ecosystems (GDEs). The study of GDEs is limited; therefore, integrated, interdisciplinary environmental approaches that guarantee their monitoring and management amid current climate and anthropogenic changes are needed. A new geospatial method with an integrated and temporal approach was developed through a multicriteria approximation, taking into account expert opinion, remote sensing-GIS, and fieldwork to map groundwater-dependent ecosystem zones (GDEZ). A survey of experts (N = 26) was conducted to assign degrees of importance to the various geospatial parameters, and the mapping was carried out using 14 parameters. The reclassified parameters were normalized on a scale of 1 to 5 according to the degree of probability of the presence of GDE. The validation was carried out through fieldwork and statistical analysis. Then, the spatio-temporal changes amid changing GW levels were assessed using the summer season normalized difference vegetation index (NDVI). Two GDEZ maps were obtained, for 2002 and 2017, between which the high- and very-high-probability zones of GDEs decreased by 31,887 ha (~ 38%). The most sensitive temporal parameters that most influenced the spatio-temporal changes on GDEs were precipitation and land use, with rain exerting a slightly the greatest influence. It was also demonstrated that identified ecosystems decreased in area or were affected by aquifer depletion (NDVI-GW, r Pearson ≥0.74). This validated method allows spatio-temporal changes in GDEs to be mapped and analyzed at an annual scale and is transferable to other arid and semi-arid environments.

Corrigendum to "PRAK-03202: A triple antigen virus-like particle vaccine candidate against SARS CoV-2"[Heliyon 7 (10) (October 2021) e08124].

[This corrects the article DOI: 10.1016/j.heliyon.2021.e08124.].