A de novo transcription-dependent TAD boundary underpins critical multiway interactions during antibody class switch recombination.

Interactions between transcription and cohesin-mediated loop extrusion can influence 3D chromatin architecture. However, their relevance in biology is unclear. Here, we report a direct role for such interactions in the mechanism of antibody class switch recombination (CSR) at the murine immunoglobulin heavy chain locus (Igh). Using Tri-C to measure higher-order multiway interactions on single alleles, we find that the juxtaposition (synapsis) of transcriptionally active donor and acceptor Igh switch (S) sequences, an essential step in CSR, occurs via the interaction of loop extrusion complexes with a de novo topologically associating domain (TAD) boundary formed via transcriptional activity across S regions. Surprisingly, synapsis occurs predominantly in proximity to the 3' CTCF-binding element (3'CBE) rather than the Igh super-enhancer, suggesting a two-step mechanism whereby transcription of S regions is not topologically coupled to synapsis, as has been previously proposed. Altogether, these insights advance our understanding of how 3D chromatin architecture regulates CSR.

Protein kinase PfPK2 mediated signalling is critical for host erythrocyte invasion by malaria parasite.

Signalling pathways in malaria parasite remain poorly defined and major reason for this is the lack of understanding of the function of majority of parasite protein kinases and phosphatases in parasite signalling and its biology. In the present study, we have elucidated the function of Protein Kinase 2 (PfPK2), which is known to be indispensable for the survival of human malaria parasite Plasmodium falciparum. We demonstrate that it is involved in the invasion of host erythrocytes, which is critical for establishing infection. In addition, PfPK2 may also be involved in the maturation of the parasite post-invasion. PfPK2 regulates the release of microneme proteins like Apical Membrane Antigen 1 (AMA1), which facilitates the formation of Tight Junction between the merozoite and host erythrocyte- a key step in the process of invasion. Comparative phosphoproteomics studies revealed that PfPK2 may be involved in regulation of several key proteins involved in invasion and signalling. Furthermore, PfPK2 regulates the generation of cGMP and the release of calcium in the parasite, which are key second messengers for the process of invasion. These and other studies have shed light on a novel signalling pathway in which PfPK2 acts as an upstream regulator of important cGMP-calcium signalling, which plays an important role in parasite invasion.

A universal biomolecular integral feedback controller for robust perfect adaptation.

Homeostasis is a recurring theme in biology that ensures that regulated variables robustly-and in some systems, completely-adapt to environmental perturbations. This robust perfect adaptation feature is achieved in natural circuits by using integral control, a negative feedback strategy that performs mathematical integration to achieve structurally robust regulation1,2. Despite its benefits, the synthetic realization of integral feedback in living cells has remained elusive owing to the complexity of the required biological computations. Here we prove mathematically that there is a single fundamental biomolecular controller topology3 that realizes integral feedback and achieves robust perfect adaptation in arbitrary intracellular networks with noisy dynamics. This adaptation property is guaranteed both for the population-average and for the time-average of single cells. On the basis of this concept, we genetically engineer a synthetic integral feedback controller in living cells4 and demonstrate its tunability and adaptation properties. A growth-rate control application in Escherichia coli shows the intrinsic capacity of our integral controller to deliver robustness and highlights its potential use as a versatile controller for regulation of biological variables in uncertain networks. Our results provide conceptual and practical tools in the area of cybergenetics3,5, for engineering synthetic controllers that steer the dynamics of living systems3-9.

Controlling CAR-T cell activity and specificity with synthetic SparX adapters.

While conventional chimeric antigen-receptor (CAR)-T therapies have shown remarkable clinical activity in some settings, they can induce severe toxicities and are rarely curative. To address these challenges, we developed a controllable cell therapy where synthetic D-domain-containing proteins (soluble protein antigen-receptor X-linker [SparX]) bind one or more tumor antigens and mark those cells for elimination by genetically modified T cells (antigen-receptor complex [ARC]-T). The chimeric antigen receptor was engineered with a D-domain that specifically binds to the SparX protein via a unique TAG, derived from human alpha-fetoprotein. The interaction is mediated through an epitope on the TAG that is occluded in the native alpha-fetoprotein molecule. In vitro and in vivo data demonstrate that the activation and cytolytic activity of ARC-T cells is dependent on the dose of SparX protein and only occurs when ARC-T cells are engaged with SparX proteins bound to antigen-positive cells. ARC-T cell specificity was also redirected in vivo by changing SparX proteins that recognized different tumor antigens to combat inherent or acquired tumor heterogeneity. The ARC-SparX platform is designed to expand patient and physician access to cell therapy by controlling potential toxicities through SparX dosing regimens and enhancing tumor elimination through sequential or simultaneous administration of SparX proteins engineered to bind different tumor antigens.

Universal structural requirements for maximal robust perfect adaptation in biomolecular networks.

Adaptation is a running theme in biology. It allows a living system to survive and thrive in the face of unpredictable environments by maintaining key physiological variables at their desired levels through tight regulation. When one such variable is maintained at a certain value at the steady state despite perturbations to a single input, this property is called robust perfect adaptation (RPA). Here we address and solve the fundamental problem of maximal RPA (maxRPA), whereby, for a designated output variable, RPA is achieved with respect to perturbations in virtually all network parameters. In particular, we show that the maxRPA property imposes certain structural constraints on the network. We then prove that these constraints are fully characterized by simple linear algebraic stoichiometric conditions which differ between deterministic and stochastic descriptions of the dynamics. We use our results to derive a new internal model principle (IMP) for biomolecular maxRPA networks, akin to the celebrated IMP in control theory. We exemplify our results through several known biological examples of robustly adapting networks and construct examples of such networks with the aid of our linear algebraic characterization. Our results reveal the universal requirements for maxRPA in all biological systems, and establish a foundation for studying adaptation in general biomolecular networks, with important implications for both systems and synthetic biology.

Anti-tau antibodies targeting a conformation-dependent epitope selectively bind seeds.

Neurodegenerative tauopathies are caused by the transition of tau protein from a monomer to a toxic aggregate. They include Alzheimer disease (AD), progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), and Pick disease (PiD). We have previously proposed that tau monomer exists in two conformational ensembles: an inert form (Mi), which does not self-assemble, and seed-competent form (Ms), which self-assembles and templates ordered assembly growth. We proposed that cis/trans isomerization of tau at P301, the site of dominant disease-associated S/L missense mutations, might underlie the transition of wild-type tau to a seed-competent state. Consequently, we created monoclonal antibodies using non-natural antigens consisting of fluorinated proline (P∗) at the analogous P270 in repeat 1 (R1), biased toward the trans-configuration at either the R1/R2 (TENLKHQP∗GGGKVQIINKK) or the R1/R3 (TENLKHQP∗GGGKVQIVYK) interfaces. Two antibodies, MD2.2 and MD3.1, efficiently immunoprecipitated soluble seeds from AD and PSP but not CBD or PiD brain samples. The antibodies efficiently stained brain samples of AD, PSP, and PiD, but not CBD. They did not immunoprecipitate or immunostain tau from the control brain. Creation of potent anti-seed antibodies based on the trans-proline epitope implicates local unfolding around P301 in pathogenesis. MD2.2 and MD3.1 may also be useful for therapy and diagnosis.

Evaluating the utility of brightfield image data for mechanism of action prediction.

Fluorescence staining techniques, such as Cell Painting, together with fluorescence microscopy have proven invaluable for visualizing and quantifying the effects that drugs and other perturbations have on cultured cells. However, fluorescence microscopy is expensive, time-consuming, labor-intensive, and the stains applied can be cytotoxic, interfering with the activity under study. The simplest form of microscopy, brightfield microscopy, lacks these downsides, but the images produced have low contrast and the cellular compartments are difficult to discern. Nevertheless, by harnessing deep learning, these brightfield images may still be sufficient for various predictive purposes. In this study, we compared the predictive performance of models trained on fluorescence images to those trained on brightfield images for predicting the mechanism of action (MoA) of different drugs. We also extracted CellProfiler features from the fluorescence images and used them to benchmark the performance. Overall, we found comparable and largely correlated predictive performance for the two imaging modalities. This is promising for future studies of MoAs in time-lapse experiments for which using fluorescence images is problematic. Explorations based on explainable AI techniques also provided valuable insights regarding compounds that were better predicted by one modality over the other.

Effectiveness of Eupatorium Perfoliatum 30C in Preventing Dengue Fever-A Prospective, Community-Based, Open Label, Parallel Cohort Study in Delhi, India.

OBJECTIVE: The study was undertaken to evaluate the protective effect of Eupatorium perfoliatum (EP) 30C on the incidence of dengue fever. DESIGN: This study was designed as a prospective, open label, parallel cohort study. INTERVENTIONS: Participants were enrolled in two medicine cohort (MC) communities and one control cohort (CC) community. Participants in MC who were more than 5 years of age received four medicated globules and those aged between 1 and 5 years received two medicated globules of EP 30C once a week for 10 weeks. Participants in CC received no medical intervention. Both cohorts received information and educational material regarding dengue. OUTCOME MEASURES: The primary outcome was incidence of dengue cases based on laboratory confirmation or the clinical definition of dengue as per the case definition notified by the National Vector-Borne Disease Control Program, Government of India, during the 14 weeks of intervention and observation. RESULTS: The analysis included 20,607 participants residing in three slums of Delhi, of which MC and CC included 15,298 and 5,309 participants respectively. The overall protective effect of EP 30C in MC against probable/laboratory-confirmed dengue was 59.59% (95% confidence interval [CI], 50.74 to 66.85, p = 0.0001). The overall protective effect of EP 30C against laboratory-confirmed dengue was 72.78% (95% CI, 54.45 to 83.74, p = 0.0001): 26 cases per 15,298 (0.13 per 1,000 person-weeks) in MC versus 33 cases per 5,309 (0.46 per 1,000 person-weeks) in CC. CONCLUSION: The use of EP 30C was associated with some protection against probable and laboratory-confirmed dengue.

Interpretation of organizational role of proteins on E. coli nucleoid via Hi-C integrated model.

Several proteins in Escherichia coli work together to maintain the complex organization of its chromosome. However, the individual roles of these so-called nucleoid-associated proteins (NAPs) in chromosome architectures are not well characterized. Here, we quantitatively dissect the organizational roles of Heat Unstable (HU), a ubiquitous protein in E. coli and MatP, an NAP specifically binding to the Ter macrodomain of the chromosome. Toward this end, we employ a polymer physics-based computer model of wild-type chromosome and their HU- and MatP-devoid counterparts by incorporating their respective experimentally derived Hi-C contact matrix, cell dimensions, and replication status of the chromosome commensurate with corresponding growth conditions. Specifically, our model for the HU-devoid chromosome corroborates well with the microscopy observation of compaction of chromosome at short genomic range but diminished long-range interactions, justifying precedent hypothesis of segregation defect upon HU removal. Control simulations point out that the change in cell dimension and chromosome content in the process of HU removal holds the key to the observed differences in chromosome architecture between wild-type and HU-devoid cells. On the other hand, simulation of MatP-devoid chromosome led to locally enhanced contacts between Ter and its flanking macrodomains, consistent with previous recombination assay experiments and MatP's role in insulation of the Ter macrodomain from the rest of the chromosome. However, the simulation indicated no change in matS sites' localization. Rather, a set of designed control simulations showed that insulation of Ter is not caused by bridging of distant matS sites, also lending credence to a recent mobility experiment on various loci of the E. coli chromosome. Together, the investigations highlight the ability of an integrative model of the bacterial genome in elucidating the role of NAPs and in reconciling multiple experimental observations.

The Occurrence Birth-Death Process for Combined-Evidence Analysis in Macroevolution and Epidemiology.

Phylodynamic models generally aim at jointly inferring phylogenetic relationships, model parameters, and more recently, the number of lineages through time, based on molecular sequence data. In the fields of epidemiology and macroevolution, these models can be used to estimate, respectively, the past number of infected individuals (prevalence) or the past number of species (paleodiversity) through time. Recent years have seen the development of "total-evidence" analyses, which combine molecular and morphological data from extant and past sampled individuals in a unified Bayesian inference framework. Even sampled individuals characterized only by their sampling time, that is, lacking morphological and molecular data, which we call occurrences, provide invaluable information to estimate the past number of lineages. Here, we present new methodological developments around the fossilized birth-death process enabling us to (i) incorporate occurrence data in the likelihood function; (ii) consider piecewise-constant birth, death, and sampling rates; and (iii) estimate the past number of lineages, with or without knowledge of the underlying tree. We implement our method in the RevBayes software environment, enabling its use along with a large set of models of molecular and morphological evolution, and validate the inference workflow using simulations under a wide range of conditions. We finally illustrate our new implementation using two empirical data sets stemming from the fields of epidemiology and macroevolution. In epidemiology, we infer the prevalence of the coronavirus disease 2019 outbreak on the Diamond Princess ship, by taking into account jointly the case count record (occurrences) along with viral sequences for a fraction of infected individuals. In macroevolution, we infer the diversity trajectory of cetaceans using molecular and morphological data from extant taxa, morphological data from fossils, as well as numerous fossil occurrences. The joint modeling of occurrences and trees holds the promise to further bridge the gap between traditional epidemiology and pathogen genomics, as well as paleontology and molecular phylogenetics. [Birth-death model; epidemiology; fossils; macroevolution; occurrences; phylogenetics; skyline.].

Outcomes following cardiac resynchronisation therapy in older people.

INTRODUCTION: Older patients may be less likely to receive cardiac resynchronisation therapy (CRT) for the management of heart failure. We aimed to describe the differences in clinical response, complications, and subsequent outcomes following CRT implantation compared to younger patients. METHODS: We conducted a retrospective cohort study of unselected, consecutive patients implanted with CRT devices between March 2008 and July 2017. We recorded complications, symptomatic and echocardiographic response, hospitalisation for heart failure, and all-cause mortality comparing patients aged <70, 70-79 and ≥ 80 years. RESULTS: Five hundred and seventy-four patients (median age 76 years [interquartile range 68-81], 73.3% male) received CRT. At baseline, patients aged ≥80 years had worse symptoms, were more likely to have co-morbidities, and less likely to be receiving comprehensive medical therapy, although left ventricular function was similar. Older patients were less likely to receive CRT-defibrillators compared to CRT-pacemakers. Complications were infrequent and not more common in older patients. Age was not a predictor of symptomatic or echocardiographic response to CRT (67.2%, 71.2% and 62.6% responders in patients aged <70, 70-79 and ≥ 80 years, respectively; P = 0.43), and time to first heart failure hospitalisation was similar across age groups (P = 0.28). Ten-year survival was lower for older patients (49.9%, 23.9% and 6.8% in patients aged <70, 70-79 and ≥ 80 years, respectively; P < 0.001). CONCLUSIONS: The benefits of CRT on symptoms and left ventricular function were not different in older patients despite a greater burden of co-morbidities and less optimal medical therapy. These findings support the use of CRT in an ageing population.

Vitamin D deficiency in patients with diabetes and its correlation with water fluoride levels.

Chronic exposure to fluoride through drinking water has been linked to insulin resistance and resulting type 2 diabetes mellitus (T2DM). Here, we aim to study the impact of water fluoride levels on blood glucose and vitamin D levels. A hospital-based study was conducted on diabetic patients (n = 303) at All India Institute of Medical Sciences (AIIMS), Raebareli outstation patient department (OPD). The relationship between vitamin D or fasting blood glucose levels (BGLs) with water fluoride levels was estimated through Spearman's rank correlation. We found a significant negative correlation between water fluoride and vitamin D levels [rs = -0.777, p-value < 0.001] and a positive correlation between water fluoride and fasting BGLs [rs = 0.178, p-value <0.05]. The participants residing in fluoride-endemic areas (F > 1.5 mg/L) had higher odds of severe vitamin D deficiency (odds ratio: 5.07, 95% CI: 1.9-13.2, p-value = 0.0009). The results demonstrate that vitamin D deficiency and fasting BGLs are significantly associated with water fluoride levels. This study signifies the role of fluoride toxicity in poor glycemic control and derived vitamin D deficiency. Vitamin D supplementation and the application of standard household water purification devices are recommended to tackle vitamin D deficiency in fluoride-endemic areas.

A Hi-C data-integrated model elucidates E. coli chromosome's multiscale organization at various replication stages.

The chromosome of Escherichia coli is riddled with multi-faceted complexity. The emergence of chromosome conformation capture techniques are providing newer ways to explore chromosome organization. Here we combine a beads-on-a-spring polymer-based framework with recently reported Hi-C data for E. coli chromosome, in rich growth condition, to develop a comprehensive model of its chromosome at 5 kb resolution. The investigation focuses on a range of diverse chromosome architectures of E. coli at various replication states corresponding to a collection of cells, individually present in different stages of cell cycle. The Hi-C data-integrated model captures the self-organization of E. coli chromosome into multiple macrodomains within a ring-like architecture. The model demonstrates that the position of oriC is dependent on architecture and replication state of chromosomes. The distance profiles extracted from the model reconcile fluorescence microscopy and DNA-recombination assay experiments. Investigations into writhe of the chromosome model reveal that it adopts helix-like conformation with no net chirality, earlier hypothesized in experiments. A genome-wide radius of gyration map captures multiple chromosomal interaction domains and identifies the precise locations of rrn operons in the chromosome. We show that a model devoid of Hi-C encoded information would fail to recapitulate most genomic features unique to E. coli.

Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review.

The ripeness of bananas is the most significant factor affecting nutrient composition and demand. Conventionally, cutting and ripeness analysis requires expert knowledge and substantial human intervention, and different studies have been conducted to automate and substantially reduce human effort. Using the Preferred Reporting Items for the Systematic Reviews approach, 1548 studies were extracted from journals and conferences, using different research databases, and 35 were included in the final review for key parameters. These studies suggest the dominance of banana fingers as input data, a sensor camera as the preferred capturing device, and appropriate features, such as color, that can provide better detection. Among six stages of ripeness, the studies employing the four mentioned stages performed better in terms of accuracy and coefficient of determination value. Among all the works for detecting ripeness stages prediction, convolutional neural networks were found to perform sufficiently well with large datasets, whereas conventional artificial neural networks and support vector machines attained better performance for sensor-related data. However, insufficient information on the dataset and capturing device, limited data availability, and exploitation of data augmentation techniques are limitations in existing studies. Thus, effectively addressing these shortcomings and close collaboration with experts to predict the ripeness stages should be pursued.

Analysis of Dielectric Parameters of Fe2O3-Doped Polyvinylidene Fluoride/Poly(methyl methacrylate) Blend Composites.

In this paper, we report the effect of metal oxide (Fe2O3) loading in different weight ratios (0.5%, 1%, 2%, and 4%) on the structural and electrical parameters, viz., the complex dielectric constant, electric modulus spectra, and the AC conductivity, of polymeric composites of PVDF/PMMA (30/70 weight ratio) blend. The structural and geometric measurements have been analyzed with the help of peak location, peak intensity, and peak shape obtained from XRD as well as from FTIR spectra. The electrical properties have been investigated using an impedance analyzer in the frequency range 100 Hz to 1 MHz. The real parts of the complex permittivity and the dielectric loss tangent of these materials are found to be frequency independent in the range from 20 KHz to 1 MHz, but they increase with the increase in the concentration of nano-Fe2O3. The conductivity also increases with an increased loading of Fe2O3 in PVDF/PMMA polymer blends. The electric modulus spectra were used to analyze the relaxation processes associated with the Maxwell-Wagner-Sillars mechanism and chain segmental motion in the polymer mix.

Test of a Homeopathic Algorithm for COVID-19: the Importance of a Broad Perspective.

BACKGROUND: Most of the symptoms of coronavirus disease 2019 (COVID-19) are covered by large repertory rubrics and hence many remedies have been proposed as "genus epidemicus". The aim of this study was to combine the information from various data collections to prepare a COVID-19 Bayesian mini-repertory/an algorithm-based application (app) and test it. METHODS: In July 2021, 1,161 COVID-19 cases from 100 practitioners globally were combined. These data were used to calculate "condition-confined" likelihood ratios (LRs) for 59 symptoms of COVID-19. Out of these, 35 symptoms of the 11 medicines that had at least 20 cases each were considered. The information was entered in a spreadsheet (algorithm) to calculate combined LRs of specific combinations of symptoms. The algorithm contained the medicines Arsenicum album, Belladonna, Bryonia alba, Camphora, Gelsemium sempervirens, Hepar sulphuris, Mercurius solubilis, Nux vomica, Phosphorus, Pulsatilla and Rhus toxicodendron. To test concordance, the doctors were then invited to re-enter the symptoms of their cases into this algorithm. RESULTS: The algorithm was re-tested on 358 cases, and concordance was seen in 288 cases. On analysis of the data, bias was noticed in the Merc group, which was therefore excluded from the algorithm. The remaining 10 medicines, representing 81.8% of all cases, were included in the preparation of the next version of the homeopathic mini-repertory and app. CONCLUSION: The Bayesian mini-repertory and app is based on qualitative clinical experiences of various doctors in COVID-19 and gives indications for specific medicines for common COVID-19 symptoms. It is freely available [English: https://hpra.co.uk/; Spanish: https://hpra.co.uk/es ] for further testing and utilization by the profession.

Homeopathic Medicines in Second Wave of COVID-19: Prognostic Factor Research.

BACKGROUND: The clinical profile and course of COVID-19 evolved perilously in a second wave, leading to the use of various treatment modalities that included homeopathy. This prognostic factor research (PFR) study aimed to identify clinically useful homeopathic medicines in this second wave. METHODS: This was a retrospective, multi-centred observational study performed from March 2021 to May 2021 on confirmed COVID-19 cases who were either in home isolation or at COVID Care Centres in Delhi, India. The data were collected from integrated COVID Care Centres where homeopathic medicines were prescribed along with conventional treatment. Only those cases that met a set of selection criteria were considered for analysis. The likelihood ratio (LR) was calculated for the frequently occurring symptoms of the prescribed medicines. An LR of 1.3 or greater was considered meaningful. RESULTS: Out of 769 confirmed COVID-19 cases reported, 514 cases were selected for analysis, including 467 in home isolation. The most common complaints were cough, fever, myalgia, sore throat, loss of taste and/or smell, and anxiety. Most cases improved and there was no adverse reaction. Certain new symptoms, e.g., headache, dryness of mouth and conjunctivitis, were also seen. Thirty-nine medicines were prescribed, the most frequent being Bryonia alba followed by Arsenicum album, Pulsatilla nigricans, Belladonna, Gelsemium sempervirens, Hepar sulphuris, Phosphorus, Rhus toxicodendron and Mercurius solubilis. By calculating LR, the prescribing indications of these nine medicines were ascertained. CONCLUSION: Add-on use of homeopathic medicines has shown encouraging results in the second wave of COVID-19 in integrated care facilities. Further COVID-related research is required to be undertaken on the most commonly prescribed medicines.

Iron Deficiency Anemia as a Potential Risk Factor for Unprovoked DVT in Young Patients: A Case Series.

Deep vein thrombosis (DVT) is a serious and potentially life-threatening condition due to the occurrence of pulmonary embolism (PEs) in the acute phase. DVT can be provoked or unprovoked. Provoked DVT can be associated with transient or persistent causes. Iron deficiency anemia (IDA) with reactive thrombocytosis can act as a prothrombotic condition. We present two case reports of lower limb DVT which was associated with moderate anemia. Association between IDA and thrombosis has been reviewed.

Pollution free UV-C radiation to mitigate COVID-19 transmission.

The high rate of transmission of the COVID-19 virus has brought various types of disinfection techniques, for instance, hydrogen peroxide vaporization, microwave generating steam, UV radiation, and dry heating, etc. to prevent the further transmission of the virus. The chemical-based techniques are predominantly used for sanitization of hands, buildings, hospitals, etc. However, these chemicals may affect the health of humans and the environment in unexplored aspects. Furthermore, the UV lamp-based radiation sanitization technique had been applied but has not gained larger acceptability owing to its limitation to penetrate different materials. Therefore, the optical properties of materials are especially important for the utilization of UV light on such disinfection applications. The germicidal or microorganism inactivation application of UV-C has only been in-use in a closed chamber, due to its harmful effect on human skin and the eye. However, it is essential to optimize UV for its use in an open environment for a larger benefit to mitigate the virus spread. In view of this, far UV-C (222 nm) based technology has emerged as a potential option for the sanitization in open areas and degradation of microorganisms present in aerosol during the working conditions. Hence, in the present review article, efforts have been made to evaluate the technical aspects of UV (under the different spectrum and wavelength ranges) and the control of COVID 19 virus spread in the atmosphere including the possibilities of the human body sanitization in working condition.

Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters.

The Coronavirus disease 2019 (COVID-19) pandemic has severely crippled the economy on a global scale. Effective and accurate forecasting models are essential for proper management and preparedness of the healthcare system and resources, eventually aiding in preventing the rapid spread of the disease. With the intention to provide better forecasting tools for the management of the pandemic, the current research work analyzes the effect of the inclusion of environmental parameters in the forecasting of daily COVID-19 cases. Three univariate variants of the long short-term memory (LSTM) model (basic/vanilla, stacked, and bi-directional) were employed for the prediction of daily cases in 9 cities across 3 countries with varying climatic zones (tropical, sub-tropical, and frigid), namely India (New Delhi and Nagpur), USA (Yuma and Los Angeles) and Sweden (Stockholm, Skane, Uppsala and Vastra Gotaland). The results were compared to a basic multivariate LSTM model with environmental parameters (temperature (T) and relative humidity (RH)) as additional inputs. Periods with no or minimal lockdown were chosen specifically in these cities to observe the uninhibited spread of COVID-19 and explore its dependence on daily environmental parameters. The multivariate LSTM model showed the best overall performance; the mean absolute percentage error (MAPE) showed an average of 64% improvement from other univariate models upon the inclusion of the above environmental parameters. Correlation with temperature was generally positive for the cold regions and negative for the warm regions. RH showed mixed correlations, most likely driven by its temperature dependence and effect of allied local factors. The results suggest that the inclusion of environmental parameters could significantly improve the performance of LSTMs for predicting daily cases of COVID-19, although other positive and negative confounding factors can affect the forecasting power.