Forecasting Atrial Fibrillation: The Predictive Power of N-terminal Prohormone of Brain Natriuretic Peptide in a Five-Year Study.

Introduction Atrial fibrillation (AF) is a major global health concern, and early prediction is essential for managing high-risk individuals. N-terminal prohormone of brain natriuretic peptide (NT-proBNP) has emerged as a crucial biomarker for predicting AF. While most studies have concentrated on cohorts already diagnosed with AF or other cardiac diseases, this research investigates the predictive value of NT-proBNP for AF development in a population without prior AF diagnosis. Methods and materials A five-year prospective observational study was conducted on 4090 individuals aged 45 to 75 with no previous diagnosis of AF. Baseline demographic characteristics, comorbid conditions, cardiac-specific measures, and NT-proBNP levels were systematically recorded. The primary endpoint was the onset of AF, confirmed through annual 12-lead ECG or 24-hour Holter monitoring. Univariate and multivariate analyses identified factors associated with AF onset. Results Out of the total population, 16.6% (679 individuals) developed AF. Notably, increased NT-proBNP levels (P=0.001), older age (P=0.001), and hypertension (P=0.001) were significantly associated with the onset of AF. The mean NT-proBNP levels in the AF group were significantly higher than in the non-AF group (P<0.001). The AF group also showed a higher mean age and a greater prevalence of hypertension (P<0.001 for both). Conclusion This study confirms the predictive value of NT-proBNP for AF onset in a non-AF population, highlighting older age and hypertension as significant risk factors for AF development. The findings underscore the potential of NT-proBNP not only as a predictive biomarker but also as a therapeutic target. These insights emphasize the potential role of NT-proBNP in early intervention and management strategies for AF, suggesting that future research should include additional variables, such as lifestyle factors and genetic predisposition, in assessing AF risk.

Naringenin as potent anticancer phytocompound in breast carcinoma: from mechanistic approach to nanoformulations based therapeutics.

The bioactive compounds present in citrus fruits are gaining broader acceptance in oncology. Numerous studies have deciphered naringenin's antioxidant and anticancer potential in human and animal studies. Naringenin (NGE) potentially suppresses cancer progression, thereby improving the health of cancer patients. The pleiotropic anticancer properties of naringenin include inhibition of the synthesis of growth factors and cytokines, inhibition of the cell cycle, and modification of several cellular signaling pathways. As an herbal remedy, naringenin has significant pharmacological properties, such as anti-inflammatory, antioxidant, neuroprotective, hepatoprotective, and anti-cancer activities. The inactivation of carcinogens following treatment with pure naringenin, naringenin-loaded nanoparticles, and naringenin combined with anti-cancer agents was demonstrated by data in vitro and in vivo studies. These studies included colon cancer, lung neoplasms, breast cancer, leukemia and lymphoma, pancreatic cancer, prostate tumors, oral squamous cell carcinoma, liver cancer, brain tumors, skin cancer, cervical and ovarian cancers, bladder neoplasms, gastric cancer, and osteosarcoma. The effects of naringenin on processes related to inflammation, apoptosis, proliferation, angiogenesis, metastasis, and invasion in breast cancer are covered in this narrative review, along with its potential to develop novel and secure anticancer medications.

Advancements of Glucose Monitoring Biosensor: Current State, Generations of Technological Progress, and Innovation Dynamics.

Glucose monitoring is essential for managing diabetes, and continuous glucose monitoring biosensors can offer real-time monitoring with little invasiveness. However, challenges remain in improving sensor accuracy, selectivity, and overall performance. This article aims to review current trends and recent advancements in glucose-monitoring biosensors while evaluating their benefits and limitations for diabetes monitoring. An analysis of current literature on transdermal glucose sensors was conducted, focusing on detection techniques, novel nanomaterials, and integrated sensor systems. Recent research has led to advancements in electrochemical, optical, electromagnetic, and sonochemical sensors for transdermal glucose detection. The use of novel nanomaterials and integrated sensor designs has improved sensitivity, selectivity, and accuracy. However, issues like calibration requirements, motion artifacts, and skin irritation persist. Transdermal glucose sensors show promise for non-invasive, convenient diabetes monitoring but require further enhancements to address limitations in accuracy, reliability, and biocompatibility. Continued research and innovation focusing on sensor materials, designs, and surface chemistry is needed to optimize biosensor performance and utility. The study offers a comprehensive analysis of the present status of technological advancement and highlights areas that need more research.

Unraveling the intricate relationship between lipid metabolism and oncogenic signaling pathways.

Lipids, the primary constituents of the cell membrane, play essential roles in nearly all cellular functions, such as cell-cell recognition, signaling transduction, and energy provision. Lipid metabolism is necessary for the maintenance of life since it regulates the balance between the processes of synthesis and breakdown. Increasing evidence suggests that cancer cells exhibit abnormal lipid metabolism, significantly affecting their malignant characteristics, including self-renewal, differentiation, invasion, metastasis, and drug sensitivity and resistance. Prominent oncogenic signaling pathways that modulate metabolic gene expression and elevate metabolic enzyme activity include phosphoinositide 3-kinase (PI3K)/AKT, MAPK, NF-kB, Wnt, Notch, and Hippo pathway. Conversely, when metabolic processes are not regulated, they can lead to malfunctions in cellular signal transduction pathways. This, in turn, enables uncontrolled cancer cell growth by providing the necessary energy, building blocks, and redox potentials. Therefore, targeting lipid metabolism-associated oncogenic signaling pathways could be an effective therapeutic approach to decrease cancer incidence and promote survival. This review sheds light on the interactions between lipid reprogramming and signaling pathways in cancer. Exploring lipid metabolism as a target could provide a promising approach for creating anticancer treatments by identifying metabolic inhibitors. Additionally, we have also provided an overview of the drugs targeting lipid metabolism in cancer in this review.

Understanding Whitening Loss in Self-supervised Learning.

A desirable objective in self-supervised learning (SSL) is to avoid feature collapse. Whitening loss guarantees collapse avoidance by minimizing the distance between embeddings of positive pairs under the conditioning that the embeddings from different views are whitened. In this paper, we propose a framework with an informative indicator to analyze whitening loss, which provides a clue to demystify several interesting phenomena and a pivoting point connecting to other SSL methods. We show that batch whitening (BW) based methods do not impose whitening constraints on the embedding but only require the embedding to be full-rank. This full-rank constraint is also sufficient to avoid dimensional collapse. We further demonstrate that the stable rank of the embedding is invariant during training by gradient descent, given the assumption that embedding is updated with an infinitely small learning rate. Based on our analysis, we propose channel whitening with random group partition (CW-RGP), which exploits the advantages of BW-based methods in preventing collapse and avoids their disadvantages requiring large batch size. Experimental results on ImageNet classification and COCO object detection reveal that the proposed CW-RGP possesses a promising potential for learning good representations.

Shifting the Paradigm: How Stress Hyperglycemia Alters the Landscape of Heart Failure Management.

Background Acute decompensated heart failure (ADHF) significantly contributes to global morbidity. Stress hyperglycemia (SHGL), although commonly observed in non-diabetic ADHF patients, remains underexplored. This study investigates the predictive value of SHGL for major adverse cardiac events (MACEs) and its impact on coronary intervention outcomes. Methods In this prospective observational study at a tertiary care center, 650 non-diabetic ADHF patients admitted for coronary intervention between April 2021 and April 2022 were assessed. SHGL was defined by random blood sugar levels >140 mg/dl. We monitored the incidence of MACEs, including cardiac death, non-fatal myocardial infarction, and heart failure rehospitalization, alongside the success rates of coronary revascularizations over 12 months. Results SHGL was present in 54% of patients (n=352) and was significantly associated with increased MACEs (p<0.001), higher rehospitalization rates (p<0.01), and lower success in revascularization (p<0.05). Using logistic regression, SHGL, age >65, and prior heart failure hospitalization were identified as independent predictors of MACEs. Statistical analyses were performed using two-tailed Mann-Whitney U tests, with significance levels set at p<0.05 for noteworthy findings and p<0.01 or p<0.001 for highly significant findings. Conclusions SHGL significantly impacts coronary intervention outcomes and the future prognosis of heart failure in non-diabetic ADHF patients, identifying it as a critical, modifiable risk factor. These findings advocate integrating SHGL management into ADHF care, emphasizing the need for further research to develop standardized treatment protocols. Proper management of SHGL could potentially improve patient outcomes, highlighting the importance of metabolic control in heart failure management.

Deciphering the Correlation between the Emergence of Lung Carcinoma Associated with Tuberculosis-related Inflammation.

Lung cancer and tuberculosis (TB) are classified as the second-most life-threatening diseases globally. They both are exclusively represented as major public health risks and might exhibit similar symptoms, occasionally diagnosed simultaneously. Several epidemiological studies suggest that TB is a significant risk factor for the progression of lung cancer. The staggering mortality rates of pulmonary disorders are intrinsically connected to lung cancer and TB. Numerous factors play a pivotal role in the development of TB and may promote lung carcinogenesis, particularly among the geriatric population. Understanding the intricacies involved in the association between lung carcinogenesis and TB has become a crucial demand of current research. Consequently, this study aims to comprehensively review current knowledge on the relationship between tuberculosis-related inflammation and the emergence of lung carcinoma, highlighting the impact of persistent inflammation on lung tissue, immune modulation, fibrosis, aspects of reactive oxygen species, and an altered microenvironment that are linked to the progression of tuberculosis and subsequently trigger lung carcinoma.

Ferroptosis targeting natural compounds as a promising approach for developing potent liver cancer agents.

Liver cancer is the second leading cause of cancer-related death worldwide. However, treatment options, including surgical resection, transplantation, and molecular drug therapies, are of limited effectiveness. Recent studies have demonstrated that suppressing ferroptosis might be a pivotal signal for liver cancer initiation, thus providing a new way to combat liver cancer. Ferroptosis is a distinct form of controlled cell death that differs from conventional cell death routes like apoptosis, necrosis, and pyroptosis. It results from intracellular iron overload, which raises iron-dependent reactive oxygen species. This, in turn, leads to the accumulation of lipid peroxides that further result in oxidative damage to cell membranes, disrupt normal functioning, and ultimately speed up the ferroptosis phenomenon. Ferroptosis regulation is intricately linked to cellular physiological processes, encompassing iron metabolism, lipid metabolism, and the equilibrium between oxygen-free radical reactions and lipid peroxidation. This review intends to summarize the natural compounds targeting ferroptosis in liver cancer to offer new therapeutic ideas for liver cancer. Furthermore, it serves as the foundation for identifying and applying chemical medicines and natural chemicals that target ferroptosis to treat liver cancer efficiently.

UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation.

Owing to the success of transformer models, recent works study their applicability in 3D medical segmentation tasks. Within the transformer models, the self-attention mechanism is one of the main building blocks that strives to capture long-range dependencies, compared to the local convolutional-based design. However, the self-attention operation has quadratic complexity which proves to be a computational bottleneck, especially in volumetric medical imaging, where the inputs are 3D with numerous slices. In this paper, we propose a 3D medical image segmentation approach, named UNETR++, that offers both high-quality segmentation masks as well as efficiency in terms of parameters, compute cost, and inference speed. The core of our design is the introduction of a novel efficient paired attention (EPA) block that efficiently learns spatial and channel-wise discriminative features using a pair of inter-dependent branches based on spatial and channel attention. Our spatial attention formulation is efficient and has linear complexity with respect to the input. To enable communication between spatial and channel-focused branches, we share the weights of query and key mapping functions that provide a complimentary benefit (paired attention), while also reducing the complexity. Our extensive evaluations on five benchmarks, Synapse, BTCV, ACDC, BraTS, and Decathlon-Lung, reveal the effectiveness of our contributions in terms of both efficiency and accuracy. On Synapse, our UNETR++ sets a new state-of-the-art with a Dice Score of 87.2%, while significantly reducing parameters and FLOPs by over 71%, compared to the best method in the literature. Our code and models are available at: https://tinyurl.com/2p87x5xn.

Review Deciphering the Potential of Nanotherapeutics in Lung Cancer Management.

Lung cancer remains a formidable challenge in oncology, necessitating the develop-ment of more effective prognostic and diagnostic techniques due to inefficient conventional therapeutic approaches and inadequate methods for early lung cancer diagnosis. Despite im-mense progress in the development of innovative strategies to alleviate the impact of this devas-tating disease, the outcomes, unfortunately, remain unsatisfactory, particularly in targeted drug delivery methods. Consequently, nanotechnology has emerged as a revolutionary force in cancer research to develop more effective targeted drug delivery tools due to its extraordinary capacity at the atomic and molecular levels. It has appeared as a beacon of hope in this area of unmet need, providing innovative ways for the prognosis and diagnosis of lung carcinoma. Therefore, this comprehensive review delves into the evolving field of nano-based therapeutics, shedding light on their potential to transform lung cancer treatment. This study meticulously explores the most promising nano-based strategies that have been extensively linked with the treatment of lung carcinoma and mainly emphasizes targeted drug delivery methods and therapies. Addition-ally, this review encapsulates the favorable results of clinical trials, which support the potential pathways for further development of nanotherapeutics in lung cancer management.

Learnable weight initialization for volumetric medical image segmentation.

Hybrid volumetric medical image segmentation models, combining the advantages of local convolution and global attention, have recently received considerable attention. While mainly focusing on architectural modifications, most existing hybrid approaches still use conventional data-independent weight initialization schemes which restrict their performance due to ignoring the inherent volumetric nature of the medical data. To address this issue, we propose a learnable weight initialization approach that utilizes the available medical training data to effectively learn the contextual and structural cues via the proposed self-supervised objectives. Our approach is easy to integrate into any hybrid model and requires no external training data. Experiments on multi-organ and lung cancer segmentation tasks demonstrate the effectiveness of our approach, leading to state-of-the-art segmentation performance. Our proposed data-dependent initialization approach performs favorably as compared to the Swin-UNETR model pretrained using large-scale datasets on multi-organ segmentation task. Our source code and models are available at: https://github.com/ShahinaKK/LWI-VMS.

The Impact of Age, Comorbidities, and Discharge Timing on Clinical Outcomes Following Elective Percutaneous Coronary Intervention.

Background The adoption of same-day discharge (SDD) in elective percutaneous coronary intervention (PCI) procedures offers potential benefits in terms of patient satisfaction and reduced healthcare costs. Despite these advantages, the safety and efficacy of SDD, especially among patients with diverse health profiles, are not fully understood. This study investigates the effects of patient-specific factors, including age, comorbidities, and discharge timing, on the clinical outcomes of elective PCI, focusing on the viability of SDD. Methods A prospective study was carried out at Lady Reading Hospital, Peshawar, Pakistan, involving 220 patients undergoing elective PCI from January to June 2023. This research compared the clinical outcomes of patients discharged on the same day with those who had extended hospital stays, examining the impact of age, comorbidities, and PCI success. Main outcome measures included post-procedure complications and hospital readmissions within 30 days. Results The study enrolled participants with an average age of 62 years, the majority (88%, n=194/220) of whom had comorbidities. Interestingly, 16% (n=35/220) of the participants were discharged on the same day, while the rest stayed longer in the hospital. Notably, those in the SDD group experienced significantly more complications and readmissions, with 95.14% (n=33/36) compared to only 16.22% (n=30/184) in their counterparts. Factors such as age, comorbidities, success of PCI, timing of discharge, and patient satisfaction emerged as significant predictors of the observed outcomes. Conclusion This study highlights the essential role of personalized care in discharge planning following elective PCI, advocating for a cautious approach towards SDD, especially for older patients and those with multiple health issues.

Evaluating Catheter Ablation Versus Conventional Management for Ventricular Arrhythmias in Arrhythmogenic Right Ventricular Cardiomyopathy: A Five-Year Retrospective Cohort Study.

Background Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a challenging genetic disorder marked by ventricular arrhythmias and sudden cardiac death, particularly in athletes and young adults. Despite its clinical significance, the relative effectiveness and safety of catheter ablation versus conventional management in ARVC are not fully delineated. Objective This study evaluates the efficacy and safety of catheter ablation compared to conventional management in reducing ventricular arrhythmias and improving patient outcomes over five years in ARVC patients. Methods In a retrospective cohort design at Lady Reading Hospital, Peshawar, we analyzed 120 ARVC patients from January 2018 to December 2023. Patients were divided into two groups: those undergoing catheter ablation and those receiving conventional management. Primary outcomes assessed were recurrence of ventricular arrhythmias, procedural complications, hospitalization duration, and mortality rates. Logistic regression was adjusted for demographics and clinical variables. Results Catheter ablation significantly lowered the recurrence of ventricular arrhythmias (20% vs. 55%, p<0.01) and reduced hospital stay duration (4 ± 2 days vs. 7 ± 3 days, p<0.05). A trend toward reduced five-year mortality was observed in the catheter ablation group (5% vs. 15%, p=0.07). Age, New York Heart Association class, and exercise capacity emerged as significant predictors of outcomes. Conclusions Catheter ablation outperforms conventional management in reducing the recurrence of ventricular arrhythmias and hospitalization in ARVC patients, with a promising trend toward enhanced survival. These findings advocate for personalized management strategies in ARVC, highlighting the necessity for further research to establish the long-term benefits of catheter ablation.

Emerging trends of phytochemicals as ferroptosis modulators in cancer therapy.

Ferroptosis, a novel form of regulated cell death characterized by dependence on iron and lipid peroxidation, has been implicated in a wide range of clinical conditions including neurological diseases, cardiovascular disorders, acute kidney failure, and various types of cancer. Therefore, it is critical to suppress cancer progression and proliferation. Ferroptosis can be triggered in cancer cells and some normal cells by synthetic substances, such as erastin, Ras-selective lethal small molecule-3, or clinical pharmaceuticals. Natural bioactive compounds are traditional drug discovery tools, and some have been therapeutically used as dietary additives or pharmaceutical agents against various malignancies. The fact that natural products have multiple targets and minimal side effects has led to notable advances in anticancer research. Research has indicated that ferroptosis can also be induced by natural compounds during cancer treatment. In this review, we focused on the most recent developments in emerging molecular processes and the significance of ferroptosis in cancer. To provide new perspectives on the future development of ferroptosis-related anticancer medications, we also provide a summary of the implications of natural phytochemicals in triggering ferroptosis through ROS production and ferritinophagy induction in a variety of malignancies.

A mechanistic review of the pharmacological potential of narirutin: a dietary flavonoid.

Flavanones, a type of polyphenol, are found in substantial amounts in citrus fruits. When high- or moderate-dose orange juice consumption occurs, flavanones make up a significant portion of the total polyphenols in plasma. Disaccharide derivative narirutin, mainly dihydroxy flavanone, is found in citrus fruits. The substantial chemotherapeutic potential of narirutin has been amply demonstrated by numerous experimental studies. Consequently, the purpose of this study is to compile the research that has already been done showing narirutin to be a promising anticancer drug, with its mechanism of action being documented in treatment plans for various cancer forms. Narirutin functions in a variety of cancer cells by regulating several pathways that include cell cycle arrest, apoptosis, antiangiogenic, antimetastatic, and DNA repair. Narirutin has been shown to modify many molecular targets linked to the development of cancer, including drug transporters, cell cycle mediators, transcription factors, reactive oxygen species, reactive nitrogen species, and inflammatory cytokines. Taken together, these reviews offer important new information about narirutin's potential as a potent and promising drug candidate for use in medicines, functional foods, dietary supplements, nutraceuticals, and other products targeted at improving the treatment of cancer.

Evaluating the Differential Risk of Contrast-Induced Nephropathy Among Diabetic and Non-diabetic Patients Following Percutaneous Coronary Intervention.

Background Contrast-induced nephropathy (CIN) significantly complicates percutaneous coronary intervention (PCI), with a higher prevalence in diabetic patients. This study compares the incidence of CIN in diabetic and non-diabetic patients undergoing PCI. Material and methods Conducted at Lady Reading Hospital, Peshawar, PAK, from January to December 2023, this observational study involved 450 adult patients with coronary artery disease (CAD) undergoing PCI. The cohort was categorized based on diabetes status, excluding patients with chronic kidney disease and those on renal replacement therapy. Baseline characteristics documented included age, gender, blood pressure, creatinine levels, and the presence of acute coronary syndrome (ACS). CIN was defined as a ≥25% increase in serum creatinine from baseline within 48-72 hours post-PCI. Data analysis was performed using the Statistical Package for the Social Sciences (IBM SPSS Statistics for Windows, IBM Corp., Version 25.0, Armonk, NY), incorporating descriptive statistics, Chi-square tests, and independent t-tests, with a significance level of p<0.05. Results The median age of the study population was 55 years. The cohort comprised 52% male (n=234) and 48% female (n=216). Notably, 33% (n=149) had ACS. Diabetic patients exhibited a significantly higher incidence of CIN post-PCI compared to non-diabetics. The highest incidence of CIN (17%, n=77) occurred in the 70+ age group. The findings highlight the criticality of renal function monitoring and procedural adjustments for diabetic patients. Conclusion Diabetic patients demonstrate an increased risk of CIN following PCI. This necessitates the development of tailored prevention strategies for this high-risk subgroup.

Terpenoid-Mediated Targeting of STAT3 Signaling in Cancer: An Overview of Preclinical Studies.

Cancer has become one of the most multifaceted and widespread illnesses affecting human health, causing substantial mortality at an alarming rate. After cardiovascular problems, the condition has a high occurrence rate and ranks second in terms of mortality. The development of new drugs has been facilitated by increased research and a deeper understanding of the mechanisms behind the emergence and advancement of the disease. Numerous preclinical and clinical studies have repeatedly demonstrated the protective effects of natural terpenoids against a range of malignancies. Numerous potential bioactive terpenoids have been investigated in natural sources for their chemopreventive and chemoprotective properties. In practically all body cells, the signaling molecule referred to as signal transducer and activator of transcription 3 (STAT3) is widely expressed. Numerous studies have demonstrated that STAT3 regulates its downstream target genes, including Bcl-2, Bcl-xL, cyclin D1, c-Myc, and survivin, to promote the growth of cells, differentiation, cell cycle progression, angiogenesis, and immune suppression in addition to chemotherapy resistance. Researchers viewed STAT3 as a primary target for cancer therapy because of its crucial involvement in cancer formation. This therapy primarily focuses on directly and indirectly preventing the expression of STAT3 in tumor cells. By explicitly targeting STAT3 in both in vitro and in vivo settings, it has been possible to explain the protective effect of terpenoids against malignant cells. In this study, we provide a complete overview of STAT3 signal transduction processes, the involvement of STAT3 in carcinogenesis, and mechanisms related to STAT3 persistent activation. The article also thoroughly summarizes the inhibition of STAT3 signaling by certain terpenoid phytochemicals, which have demonstrated strong efficacy in several preclinical cancer models.

Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma.

BACKGROUND: Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM. METHODS: In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (<18 months vs. ≥18 months) using multivariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regularization to reduce the risk of overfitting. This radiomic model in combination with clinical and histopathological data was inserted into a backward stepwise logistic regression model to assess survival. The diagnostic performance of this model was reported for the training and external validation sets. RESULTS: A total of 116 patients were included for model development and 40 patients for external testing validation. The diagnostic performance (AUC/sensitivity/specificity) of the radiomic model generated from seven texture features in determination of ≥18 months survival was 0.71/69.0/70.3. Three variables remained as independent predictors of survival, including radiomics (p = 0.004), age (p = 0.039), and MGMT status (p = 0.025). This model yielded diagnostic performance (AUC/sensitivity/specificity) of 0.77/81.0/66.0 (training) and 0.89/100/78.6 (testing) in determination of survival ≥ 18 months. CONCLUSIONS: Results show that our radiogenomic model generated from radiomic features at baseline MRI, age, and MGMT status can predict survival ≥ 18 months in patients with GBM.

Burst Image Restoration and Enhancement.

Burst Image Restoration aims to reconstruct a high-quality image by efficiently combining complementary inter-frame information. However, it is quite challenging since individual burst images often have inter-frame misalignments that usually lead to ghosting and zipper artifacts. To mitigate this, we develop a novel approach for burst image processing named BIPNet that focuses solely on the information exchange between burst frames and filter-out the inherent degradations while preserving and enhancing the actual scene details. Our central idea is to generate a set of pseudo-burst features that combine complementary information from all the burst frames to exchange information seamlessly. However, due to inter-frame misalignment, the information cannot be effectively combined in pseudo-burst. Thus, we initially align the incoming burst features regarding the reference frame using the proposed edge-boosting feature alignment. Lastly, we progressively upscale the pseudo-burst features in multiple stages while adaptively combining the complementary information. Unlike the existing works, that usually deploy single-stage up-sampling with a late fusion scheme, we first deploy a pseudo-burst mechanism followed by the adaptive-progressive feature up-sampling. The proposed BIPNet significantly outperforms the existing methods on burst super-resolution, low-light image enhancement, low-light image super-resolution, and denoising tasks. The pre-trained models and source code are available at https://github.com/akshaydudhane16/BIPNet.