Scientific Reportshttps://doi.org/10.1038/s41598-023-29658-x, published online 10 February 2023Correction to: The original version of this Article contained an error, where the term “ontogeny” was incorrectly given as “ontology.”As a result, in the title,Hermetia illucens during larval ontologyAbsence of microbiome triggers extensive changes in the transcriptional profile of now readsHermetia illucens during larval ontogenyAbsence of microbiome triggers extensive changes in the transcriptional profile of Also in the abstract:These results provide the first evidence of host functional genes regulated by microbiota in the BSF larva, further demonstrating the importance of host-microbiota interactions on host ontology and health.now reads:These results provide the first evidence of host functional genes regulated by microbiota in the BSF larva, further demonstrating the importance of host-microbiota interactions on host ontogeny and health.Also in the Introduction:However, microbiota-brain axis during ontology has not been investigated to date.now reads:However, microbiota-brain axis during ontogeny has not been investigated to date.Also in the discussion:Here, we delved into how the microbiota’s presence  or absence (axenic condition) changes the transcriptome expression profile of the BSFL during larval ontology. This enabled us to gain insight into the most affected metabolic processes and biological pathways with subsequent functional annotation (UniProt), GO enrichment, and KEGG pathways enrichment. This study offers new insight into the host-microbiota interactions affecting BSFL ontology.now reads:Here, we delved into how the microbiota’s presence  or absence (axenic condition) changes the transcriptome expression profile of the BSFL during larval ontogeny. This enabled us to gain insight into the most affected metabolic processes and biological pathways with subsequent functional annotation (UniProt), GO enrichment, and KEGG pathways enrichment. This study offers new insight into the host-microbiota interactions affecting BSFL ontogeny.and finally in the Conclusion:Our transcriptome analysis indicates that the microbiota modulates its host expression profile during ontology which suggests that the microbiota is essential to BSFL’s normal development.Transcriptome expression was mostly affected in late larval stage (day 20) for nervous system, showing a long-term effect of microbiota on its host ontology.now reads:Our transcriptome analysis indicates that the microbiota modulates its host expression profile during ontogeny which suggests that the microbiota is essential to BSFL’s normal development.Transcriptome expression was mostly affected in late larval stage (day 20) for nervous system, showing a long-term effect of microbiota on its host ontogeny.The original Article has been corrected.


Kartagener’s syndrome (KS) is a genetic disorder and a subgroup of primary ciliary dyskinesia characterized by situs inversus, chronic sinusitis and bronchiectasis. Patients with KS can develop severe bronchiectasis with end-stage lung disease due to recurrent pulmonary infections. Lung transplantation is a treatment option with good outcomes reported in the literature. Lung transplantation in such patients can be technically challenging given the dextrocardia, bronchial asymmetry and anatomical variation of major vascular structures due to situs inversus. We present a case of a 45-year-old male with KS complicated by recurrent infections and chronic respiratory failure, who successfully underwent a bilateral sequential lung transplant (BSLTx). Because of repeated infections and severe bronchiectasis, the patient's quality of life was impaired, and he was oxygen dependent. As a definitive treatment, successful lung transplantation led to a reversal of hypoxic respiratory failure and the patient's symptoms markedly improved, reinforcing data in the literature to consider lung transplantation in this patient population. Primary ciliary dyskinesia (PCD) is the congenital impairment of muco-ciliary clearance . It is gA 45-year-old Caucasian male with no pertinent past medical history with KS, whose clinical course was complicated with recurrent pulmonary infections, on long-term oxygen therapy up to 5 liters, was referred for LT. The patient was diagnosed in early adulthood and had been battling with recurrent pneumonia requiring frequent intravenous antibiotics, chest physiotherapy and hospital admissions. He did not have any smoking history or any other pertinent exposures that could cause lung disease. The patient lived on a farm and took care of cattle. He had a pet dog. The patient did not have any cardiac issues. The patient was seen by transplant pulmonology. On evaluation, the patient’s pulmonary function tests revealed severe obstruction and reduced diffusion capacity. Transthoracic echocardiography showed normal left ventricular function but a mildly dilated right atrium and mildly reduced right ventricular function. Right heart catheterization (RHC) revealed secondary pulmonary hypertension . Imaging including chest x-rays and computerized tomography (CT) scans showed dextrocardia and severe bronchiectasis Figures -3. Due tDonor-recipient size matching was done to avoid right lung volume reduction. 3D reconstruction was done to evaluate the vascular anatomy in relation to the bronchi. It showed right-sided hyp-arterial bronchus and left sided shorter ep-arterial bronchus Figures , 5.The patient was taken to the OR and a left-sided double lumen endotracheal tube was placed in the anatomical right mainstem bronchus because of reverse anatomy in the recipient. Given the moderately elevated pulmonary artery pressures, the patient was electively placed on central veno-arterial extra corporeal membrane oxygenation (ECMO) with aortic and morphological right atrial appendage cannulation. After initiation of ECMO, right native lung was deflated. The donor's right lung was implanted in a standard fashion. No size mismatch was encountered. On the left side bronchial anastomosis was performed in standard fashion and donor pericardium was tacked over the bronchial anastomosis. The pulmonary arterial anastomosis required extensive mobilization of the donor and recipient pulmonary arteries and the donor PA had to be divided in beveled fashion to accommodate for the altered anatomy.Post lung implantation and reperfusion, the patient was weaned off central VA ECMO. Upon chest closure, double lumen tube was exchanged for a single lumen endotracheal tube and bronchoscopy was performed, demonstrating patent bronchial anastomoses. Post operative imaging showed no immediate complications Figure . CT chesThe patient was extubated 48 hours post LT and got discharged home on room air within two weeks. Bronchoscopy done post operatively showed patent anastomosis Figures , 11. SurThere is a paucity of literature and guidelines regarding LT in patients with KS. The very first heart and lung transplant for a KS patient was documented in 1992 . In addiDLTx in patients with end-stage KS can be performed safely and pre-operative 3D reconstruction images can provide a better insight into the anatomical differences and potential challenges that can be encountered intraoperatively, in the setting of situs inversus and dextrocardia. Bilateral sequential lung transplant (BSLTx) with anterolateral thoracotomy offers a safe approach that forgoes sternal division and associated potential complications.


After completing of the entire 6 cycles of chemotherapy, CMR and 18F-FDG PET/CT revealed a complete remission with resolution of the symptoms.A 65-year-old woman presented to the emergency department with a 6-month history of progressive dyspnea, intermitted tachycardia, and recent weight loss. A computed tomography showed no lung embolism and no lung or abdominal tumor; however, pericardial and pleural effusion was depicted and the suspicion of a cardiac tumor in the right ventricular groove was raised. Besides confirmation of pericardial and pleural effusion Figure , CMR dep118F-FDG PET/CT is helpful to differentiate a malignant from a benign cardiac tumor and to document treatment success.3 Multimodality imaging is crucial for early noninvasive assessment of primary cardiac tumors, helps guiding further investigations, treatment decision and allows documentation of therapeutic success.Primary malignant cardiac tumors are extremely rare and establishing the diagnosis may be challenging. Angiosarcoma and lymphoma are two most common malignant primary cardiac tumors and are usually located in the right heart. CMR is the ideal modality to differentiate angiosarcoma from lymphoma.


Blunt aortic injury is the second most prevalent cause of patient fatalities post-trauma, closely following head injuries as the leading cause. In recent years, thoracic endovascular aortic repair (TEVAR) has evidently improved survival rates and reduced complications in patients suffering from blunt traumatic aortic injury (BTAI) in comparison to open surgery and non-operative management.It is difficult to characterize the appropriate criteria for the timing of TEVAR, whether early or delayed for BTAI, considering the discrepancies related to timing.Electronic databases, including PubMed, Scopus, the Cochrane Central Register of Controlled Trials (CENTRAL), and Embase, were searched through April 2023. The primary outcomes were short-term mortality and hospital and intensive care unit (ICU) stays. Time to TEVAR, acute respiratory distress syndrome, sepsis, deep vein thrombosis, delayed stroke, and renal failure were also evaluated.2=33%). In contrast, the ICU length of stay was significantly shorter in the early group ; p<0.0001; I2=55%). There was no significant difference between both groups in the presenting profile or postoperative complications.We included a total of seven studies, comprising 4177 patients who met the inclusion criteria. Short-term mortality was significantly higher in the early TEVAR group ; (1.26-2.74); p<0.001; IPatients undergoing delayed TEVAR had markedly lower mortality rates but a longer ICU stay. The need for future studies with more robust designs is imperative to investigate the factors influencing the timing of repair and the associated outcomes. Among all traumatic injuries, one of the most time-sensitive is blunt traumatic aortic injury (BTAI). It is the second most prevalent cause of patient fatalities, closely following head injuries as the leading cause . It has Thoracic endovascular aortic repair (TEVAR) has revolutionized the treatment of aortic injuries. In recent years, TEVAR has shown promising results in terms of improved survival rates and reduced complications compared to traditional approaches. It has become the mainstay of treatment for BTAI -4 and isFurthermore, it has been linked to superior outcomes in terms of reduced neurological and vascular complications in comparison to open surgery and non-operative management . DespiteConsidering such timing discrepancies, we conducted a systematic review of the available literature to investigate the optimal timing of TEVAR for patients presenting with BTAI.Materials and methodsLiterature SearchSearch terms were formulated using the Population, Intervention, Comparator, and Outcome (PICO) framework to identify papers comparing early versus delayed BTAI. The databases searched were PubMed, Scopus, the Cochrane Central Register of Controlled Trials (CENTRAL), and Embase through April 2023. The updated version of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and recommended guidelines were used to guide such a process -13.The terms used were "blunt" OR "aortic injury" OR "traumatic" OR "trauma" OR "BTAI" OR "TAI" OR "TTAL" OR "BAAI" AND "endovascular repair" OR "endovascular aortic repair" OR "endovascular surgery" OR "TEVAR" AND "early" OR "delayed" OR "late" OR "urgent" OR "emergent" OR "timing" OR "24 hours". The identified studies were screened after reading the abstracts of the studies. Duplicates were also omitted from the total number of titles and abstracts accumulated. All the studies selected for this analysis were chosen after applying the inclusion and exclusion criteria through the different screening stages.Inclusion and Exclusion CriteriaThe main inclusion criteria were studies that compared the outcomes between the early and delayed intervention of TEVAR in blunt traumatic thoracic aortic injuries. The included studies have to define the number of patients in both groups and report at least one comparative outcome of interest. If the studies did not include such comparative outcomes, they were excluded from the analysis. To be included, studies had to: 1) be clinical trials or observational cohort studies; 2) contain more than 10 patients; and 3) be studies with patients above 16 years old exposed to blunt thoracic or abdominal aortic trauma injuries that were intervened on with an early (<24 hours) or delayed (>24 hours) TEVAR.Exclusion criteria were: 1) systematic review articles, meta-analyses, review articles, case reports, editorials, study protocols, abstracts, commentaries, or letters; 2) patients under 16 years of age; 3) adult patients with open surgery intervention; 4) other causes of aortic disease like aneurysms and dissections; or 5) non-English studies.At both the abstract and full-text review stages, at least two independent reviewers examined each study, and a third reviewer resolved any conflicts. Figure Data Extraction, Outcomes, and Quality AssessmentThe cumulative data were extracted from each included article. Two independent investigators extracted data from each record. This was further revised by two other members, and the results were compared to resolve any conflicts by consensus, if present. The included studies' summaries were extracted. Baseline parameters, including patients' age, gender, Injury Severity Score (ISS), BTAI grade, and concomitant injuries, were extracted.The primary endpoint for our analysis was early mortality. Early mortality was defined as any death recorded in the study within 30 days of the procedure. Secondary endpoints included in-hospital and intensive care unit (ICU) length of stay, sepsis, delayed stroke, acute respiratory distress syndrome (ARDS), deep vein thrombosis (DVT), and renal failure.Categorical data were extracted as events and totals for each group, while continuous data were coded as mean and standard deviations. If the data were reported in other formats, the method by Wan and colleagues  was usedWe used the Newcastle-Ottawa (NOS) risk of bias assessment tool to assess the quality of the included studies. Two reviewers assessed the risk of bias independently, and the final table was assembled based on their agreement. The assessment cut-off for follow-up length was set at 30 days. We regarded follow-up as sufficient if no more than 10% of the cohort had lost follow-up.Statistical AnalysisThis meta-analysis was conducted in line with the recommendations from the Cochrane Collaboration and Meta-analysis of Observational Studies in Epidemiology -15. We uResultsIncluded Studies and Population CharacteristicsDuring the initial search, we identified 921 records for eligibility screening; 29 full-text studies were assessed for eligibility; and seven studies were eligible and included in our meta-analysis  underwent early TEVAR and 1211 (29%) underwent delayed TEVAR for BTAI repair. Only two studies reported the grade of BTAI, while another single study included only patients with grade III BTAI "pseudoaneurysm". Additional characteristics of the included studies and patient baseline data are shown in Tables Primary OutcomeAll included studies reported short-term mortality, which was significantly higher in the early TEVAR group ; 1.26-2.74); p<0.001; the proportion of the variance in the observed effect (I2) =33%). Figure 6-2.74; pThere was no significant change to the results when sensitivity analysis was performed. However, the ICU length of stay was significantly shorter in the early group (mean difference: -2.82 days), 95%CI; (-4.09 - -1.56); p<0.0001; I2=55%). Figure Secondary OutcomesSecondary outcomes were reported in four studies. There was no significant difference between early and late TEVAR groups in terms of post-procedural complications, including delayed stroke ; p=0.77; I2=0%), deep vein thrombosis ; p=0.89; I2=68%), renal failure  p=0.82; I2=37;), or sepsis ; p=0.59; I2=0%) following endovascular repair. We could not estimate differences regarding time-to-TEVAR due to the severe heterogeneity between the included studies.Table DiscussionIt was earlier thought that early TEVAR would have a better outcome as compared to delayed TEVAR due to the higher risk of complications. In this systemic review and meta-analysis, 4177 patients who underwent TEVAR for BTAI from seven studies were included in the analysis. These studies included both prospective studies and retrospective analyses. Among the patients, 2966 (71%) underwent early repair (<24 hours) for BTAI, while 1211 (29%) underwent delayed repair (>24 hours). The primary conclusions drawn from the study are as follows: 1) in the delayed repair group, the short-term mortality rate was significantly lower; 2) the ICU length of stay was significantly lower in the early group ,11,17-18TEVAR has recently emerged as a treatment option for patients with BTAI and is considered a favorable alternative to open surgery. Notably, there were no previous meta-analyses on this subject at the time this study was started.The definition of early versus delayed repair in BTAI can vary among studies, with no universally accepted cut-off. Generally, delayed repair refers to interventions performed more than 24 hours after the initial injury, although different studies may use different time intervals. The 24-hour cut-off is commonly used as a clinical recommendation to avoid detrimental incidents such as aortic rupture. The seven-day cut-off was also used to define delayed repair in BTAI. This timeframe allows for assessing outcomes in patients who did not undergo immediate intervention. It acknowledges that certain patients may benefit from a delayed approach due to associated injuries and patient stability .Blunt aortic injuries rarely occur in isolation, as patients often experience other vascular traumas . A studyThe management of blood pressure and associated injuries plays a significant role in determining patient outcomes in BTAI. The use of antihypertensive and rate-controlling agents during the delay period has become standard practice to prevent further extension of the injury . EndovasThe decreased risk of spontaneous rupture with antihypertensives is a significant factor in determining outcomes. Delayed treatment was shown to allow for intramural hematoma regression, resulting in more stable landing zones for TEVAR, which subsequently led to higher success and lower complication rates as well as greater flexibility in terms of operative timing .The evolving treatment strategies for BTAI have witnessed a shift from open surgical repair (OAR) to TEVAR and non-operative management (NOM). TEVAR is recommended for grades 2 to 4 injuries, while NOM is considered a reasonable strategy for mild injuries (grades 1 and 2). Grades 3 and 4 injuries typically require definitive repair with TEVAR or OAR, depending on the presence of comorbid injuries , 24. A sThere is an emerging role for preprocedural anticoagulation. In a study by Zambetti et al. , delayedCertain risk factors for early mortality have been identified that assist in appropriate patient selection and risk stratification. These factors include increased age, elevated creatinine levels , male gender, Injury Severity Scores reflecting the presence of non-aortic injuries, and involvement of the left subclavian artery . SurprisOverall, the option of delayed repair for TAI patients with significant associated injuries is viable, given that blood pressure is adequately managed. This allows for improved resuscitation, patient stabilization, and the identification and management of life-threatening injuries. Still, such evidence needs to be tested in studies of higher quality and prospective nature, allowing for a further in-depth understanding of other factors that moderate outcomes in this population.LimitationsThe inclusion of a retrospective, observational study design in this meta-analysis introduces certain limitations. The absence of randomized clinical trials comparing delayed and early repairs diminishes the strength of the evidence. Synthesizing data solely from retrospective observational studies introduces potential biases and confounding factors that may affect the accuracy and reliability of the findings. Furthermore, there was a notable variability in the sample sizes among the included studies, which may impact the statistical power and accuracy of the results. Consequently, the generalizability of the findings to a larger group of high-risk patients for repeat surgical intervention may be limited. It is important to acknowledge that the decision for patients to undergo TEVAR via early or delayed repair was made independently by each surgical team, based on surgeon experience, judgment, and consideration of the extent of the aortic injury. This introduces selection bias, as factors not accounted for in the analysis may influence the choice of repair timing. Additionally, only two studies reported the grade of BTAI associated with the injury, which could have served as an important confounder in the decision of early versus delayed TEVAR. Recognizing and addressing these limitations is crucial when interpreting the findings of our study. Also, future studies in this area would benefit from prospective, randomized controlled trials with larger sample sizes and individual patient data analysis to enhance the strength and validity of results.Patients undergoing delayed TEVAR had markedly lower mortality rates but a longer ICU stay. The need for future studies with more robust designs is imperative to investigate the factors influencing the timing of repair and the associated outcomes.


In contemporary biomedical research, the accurate automatic detection of cells within intricate microscopic imagery stands as a cornerstone for scientific advancement. Leveraging state-of-the-art deep learning techniques, this study introduces a novel amalgamation of Fuzzy Automatic Contrast Enhancement (FACE) and the You Only Look Once (YOLO) framework to address this critical challenge of automatic cell detection. Yeast cells, representing a vital component of the fungi family, hold profound significance in elucidating the intricacies of eukaryotic cells and human biology. The proposed methodology introduces a paradigm shift in cell detection by optimizing image contrast through optimal fuzzy clustering within the FACE approach. This advancement mitigates the shortcomings of conventional contrast enhancement techniques, minimizing artifacts and suboptimal outcomes. Further enhancing contrast, a universal contrast enhancement variable is ingeniously introduced, enriching image clarity with automatic precision. Experimental validation encompasses a diverse range of yeast cell images subjected to rigorous quantitative assessment via Root-Mean-Square Contrast and Root-Mean-Square Deviation (RMSD). Comparative analyses against conventional enhancement methods showcase the superior performance of the FACE-enhanced images. Notably, the integration of the innovative You Only Look Once (YOLOv5) facilitates automatic cell detection within a finely partitioned grid system. This leads to the development of two models—one operating on pristine raw images, the other harnessing the enriched landscape of FACE-enhanced imagery. Strikingly, the FACE enhancement achieves exceptional accuracy in automatic yeast cell detection by YOLOv5 across both raw and enhanced images. Comprehensive performance evaluations encompassing tenfold accuracy assessments and confidence scoring substantiate the robustness of the FACE-YOLO model. Notably, the integration of FACE-enhanced images serves as a catalyst, significantly elevating the performance of YOLOv5 detection. Complementing these efforts, OpenCV lends computational acumen to delineate precise yeast cell contours and coordinates, augmenting the precision of cell detection. The rapid development of single-cell sequencing technologies makes it possible to explore cell heterogeneity of genome, epigenome, transcriptome, and cell interaction/communication in the context of a specific environment in a tissue. However, the accurate detection of individual cells within complex microscopic imagery presents significant challenges. Due to various technical noises such as dropout rate, accurately measuring the expression level in a single cell is pretty challenging. Human analysis can no longer sustain the high throughput image data2. In the era of artificial intelligence and machine learning, these cutting-edge approaches are now widely applied in biological image processing, such as cell type identification, cell count, cell structure analysis, and cell reconstruction3. On the other hand, the effectiveness of image classification algorithms and the quality of the extracted image characteristics limit the accuracy and robustness of most of the methods now in use.In the realm of medical and biological research, the utilization of microscopic imagery capturing cells, tissues, and entire organisms has become a prevailing practice6. The current landscape of image processing and segmentation techniques employed for yeast images predominantly relies on traditional methodologies7, including approaches such as thresholding, edge detection, contour fitting, and watershed algorithms. However, the segmentations8 produced by these techniques often necessitate frequent human interventions, as demonstrated in multiple tests. Challenges inherent in yeast cell picture segmentation include issues such as cell crowding, irregular shapes, transparent inclusions, anomalies in visible structures, budding events, and inconsistent imaging focus. Complicating matters further, yeast cell images obtained from diverse imaging devices, such as microscopes, exhibit low contrast9 due to various factors, including the nature of the specimen, image-capturing device calibration settings, and suboptimal ambient lighting conditions.The yeast cell, with its precise developmental requirements and genetic tractability, serves as a valuable model organism in the field of biology. Optical investigations of yeast cell structure play a crucial role across various microbiological research areas, encompassing cell cycle modelling and ageing dynamics studies10. These modern approaches leverage large amounts of labelled data to train highly complex models capable of directly learning intricate patterns and features from images. Automatic yeast cell detection within images characterized by low contrast presents a substantial challenge in biological image analysis, where intricate details often become obscured, hampering accurate detection and subsequent analysis. Contrast enhancement is one of the methods of enhancing image quality and highlighting the intricacies of hidden data in the low-contrast regions of an image. Different intensity adjustments were applied to boost the visual contrast11. While enhancing the contrast depends on the selection of parameters of function coefficients that users have artificially determined. Most transformation techniques change the original intensity of the image to a new value, resulting in a distinct pattern. No single transformation function is ideal for improving contrast in each image12.In recent years, the field of automatic yeast cell detection has been revolutionized by the emergence of deep learning techniques, particularly convolutional neural networks (CNNs)13 stands out as an effective solution, optimizing contrast dynamics while preserving the inherent natural colour distribution. Moreover, the incorporation of the You Only Look Once (YOLO)14 framework, a sophisticated deep learning-based object detection methodology, serves to elevate the precision and efficiency of yeast cell identification. This combination of image enhancement and YOLO enables the precise identification and localization of yeast cells within complex biological images, contributing to the advancement of research in microbiology and biology by facilitating more accurate and insightful analyses of yeast cells in their complex environmental contexts. However, some limitations of the study include challenges tied to labelling, manpower, and time constraints during dataset preparation.The task of automatic yeast cell detection presents inherent challenges within image analysis, often exacerbated by factors such as low contrast and intricate backgrounds. To overcome these challenges, this study proposes the utilization of image enhancement techniques emerges as a pivotal strategy to enhance yeast cell visibility and accentuate their distinctive attributes. Notably, the integration of Fuzzy Automatic Contrast Enhancement (FACE)15: spatial domain processing, which operates on individual pixels and then combines neighbouring ones; and frequency domain processing, which processes the entire image collectively. Recent advancements have introduced efficient algorithms for image enhancement, with many traditional techniques being supplanted by neural network-based models16. This study explores the utilization of various image enhancement models, such as Fuzzy Automatic Contrast Enhancement (FACE), Retinex Algorithm, and Histogram Equalization (HE), to enhance the image quality of cell images.Image enhancement plays a significant role in augmenting image quality by emphasizing crucial features, mitigating secondary ones, enriching information, and refining details are undeniable. This process can be broadly categorized into two groups18. Despite its effectiveness in addressing images with extreme brightness levels, it may not precisely adjust localized areas, resulting in overexposed or underexposed images with amplified noise19. The Retinex algorithm20, founded on retinex theory21, captures the interaction of illumination and reflection to determine object color23. Single Scale Retinex (SSR) and Multiple Scale Retinex (MSR) methods exhibit different enhancement outcomes, with MSR integrating SSR outputs using varying weights to address dynamic range limitations24. To overcome colour distortion issues, Multi-Scale Retinex with Color Restoration (MSRCR) emerges as a solution, surpassing MSR in imaging quality and enhancement. The Fuzzy Automatic Contrast Enhancement (FACE)13 technique employs Fuzzy C-Means (FCM)25 clustering for pixel classification, leveraging a universal contrast enhancement variable (UCEV) to automatically maximize image entropy. This approach ensures enhanced contrast without introducing artifacts or noise, benefiting from both accuracy and smoothness in color distribution.Histogram equalization, a contrast adjustment technique using image histograms, aids in enhancing global contrast in images27.This paper presents the FACE as a contrast enhancement technique for enhancing the microscopic images of yeast cells, changing the original image pixels to a more uncongested pixel distribution. The fuzziness will maintain the smoothness of colour distribution to prevent artifacts in yeast cell images. The maximization approach using a universal contrast enhancement variable was presented to have better contrast enhancement automatically without manually fed parameters. Root-Mean-Square Deviation (RMSD) and Root-Mean-Square Contrast (RMSC) are used to evaluate the contrast of images and the difference between before and after enhancement29. Various computer vision techniques have emerged in this context for cell recognition and detection. In cell detection images, challenges arise due to the visual similarity, close proximity, and overlapping nature of cells in the 2D representation of 3D structures30. Uneven illumination, low contrast, low resolution, and diverse foreground/background intensities further complicate the task. Object detection, especially in cell detection, revolves around precise segmentation for local and global differentiation31. Given the multitude of cells in these images, precise detection is essential, particularly in overlap scenarios.Biological research has substantially adopted Machine Learning (ML) and Deep Learning (DL), notably in creating automated computer vision tools for recognizing and classifying microscopic images, with a focus on species-level identification32 conducted a comprehensive review of methods for microorganism image analysis, encompassing preparation, feature extraction, classification, and evaluation. Rea et al.33 introduced a GPU-based software utilizing the simplex method for efficient yeast cell detection in time-lapse microscopy, offering real-time analysis capabilities. An advanced RIC-Unet neural network was developed for accurate nuclei segmentation in histology images, integrating residual blocks, multi-scale, and channel attention. Van Valen et al.34 employed the DeepCell software with CNNs for cell segmentation in diverse microscopy images, sharing training insights. Hilsenbeck et al.34 introduced fastER, an efficient cell segmentation tool using SVM to estimate cell likelihood from candidate regions with texture and shape features, adopting a divide and conquer approach for optimal region selection.Li et al.32 extensively reviewed methods for analyzing microscope images of microorganisms, where he focused on techniques for classifying microorganisms using different image processing steps like preparation, feature extraction, classification, and evaluation. Rea et al.33develops a GPU-based software using the simplex method for the efficient detection of single yeast cells in time-lapse microscopy images. This approach offers significant speedup compared to CPU processing, enabling real-time analysis of microfluidics chip data. An advanced RIC-Unet neural network introduces accurate nuclei segmentation in histology images, incorporating techniques like residual blocks, multi-scale, and channel attention34. Van Valen et al. in35 use DeepCell software with CNNs for cell segmentation across diverse microscopy images, sharing training insights. Hilsenbeck et al.36 introduce fastER, an efficient cell segmentation tool using SVM to estimate cell likelihood from candidate regions with texture and shape features, using divide and conquer for optimal region selection.Li et al.37. Deep learning, particularly deep convolutional neural networks (CNNs), can automatically classify input datasets, such as images. CNNs directly learn from raw pixel data and class labels through end-to-end learning. There are primarily three families of detectors for object detection using deep learning models. These models are Region-CNN(R-CNN)39, the Single Shot Detector (SSD)40 and the Yolo series14. However, it’s worth noting that despite the remarkable capabilities of deep learning models like R-CNN, SSD, and YOLO in object detection, they often encounter challenges when dealing with low-light images. The reduced visibility in such conditions can hinder the efficiency and accuracy of object detection processes using these models. So the effect of contrast enhancement on YOLOv5 object detection was studied in this research, and in conclusion, the object detection capabilities of yeast cell image patterns are compared.Worldwide, researchers have been attracted to the fundamental and crucial object detection task in computer visionTo curate the dataset, we captured raw images using a commercially available 8-megapixel camera featuring a 1/1.8-inch sensor size and the SONY imx334 sensor (Pixel size: 2.0 × 2.0 μm). The camera was paired with a 14X-90X optical zoom achromatic field objective, seamlessly integrated into a custom-designed positioning system. To initiate the experimentation phase, a few drops of a solution containing yeast cells (Saccharomyces cerevisiae) were delicately placed onto a petri dish strategically positioned beneath the camera’s objective. This meticulous setup enabled the acquisition of high-resolution images, effectively capturing a diverse spectrum of yeast cells spanning various growth stages. After image capture, a meticulous post-processing regimen ensued, involving resizing these images to a standardized dimension of 500 pixels × 500 pixels. This resizing operation facilitated the creation of images depicting a reduced quantity of cells, thereby rendering them optimally suited for subsequent analytical procedures.The resultant dataset encompassed a comprehensive compilation of 1000 raw microscopic images of yeast cells, all meticulously formatted to the dimensions of 500 pixels × 500 pixels. This dataset served as the foundational substrate for the FACE image enhancement model. In the initial phases, the FACE methodology was applied to enhance the contrast of these 1000 raw microscopic images, culminating in creation of an enriched and enhanced image dataset. Notably, this dual dataset, consisting of original raw images and their corresponding FACE-enhanced counterparts, formed the bedrock for the subsequent development and validation of the YOLOv5 cell detection model.13 is a unique method based on fuzzy clustering and a fuzzy expansion of pixel dispersion whose schematic diagram is shown in Fig. Fuzzy Automatic Contrast Enhancement (FACE)Initially, FACE utilizes a Fuzzy C-Means (FCM) clustering technique to segment an image while categorizing CIELAB colour space pixels with similar colours into tiny image clusters with appropriate attributes. Each group’s RGB colour space pixels are dispersed away from the respective cluster’s centre to enhance the image’s contrast, which maintains the consistency of pixel colours within each cluster. To automatically improve picture contrast, a Universal Contrast Enhancement Variable (UCEV) is created and adjusted to maximize the entropy of the image. A less cluttered distribution of the image pixels guaranteed more excellent picture contrast. The suggested entropy-maximization approach can enhance image quality without manually setting control parameters., and arranged into . The inclusion of the 42, the value of Assuming there are mentclass2pt{minimThe definition of total in-group variance is shown in the Eq. .2\docume43 suggest the strength of inclusion parameters varies from 1 to 5, so for this study, the inclusion parameter is considered as 44. The following formulation of a Lagrangian equation is as follows:Different researchersA parameterized method based on the Eqs. , 4), an, an4), aEquation  states t the Eq.  states tThe Universal Contrast Enhancement Variable (UCEV) is a pivotal concept within the framework of contrast enhancement, serving as a dynamic control parameter that orchestrates the transformation of pixel values in images to achieve enhanced contrast and improved visual perception. UCEV introduces an adaptive mechanism to adjust pixel values, broadening or contracting their distribution across the color space, thereby intensifying contrast between different features or addressing issues like overly bright regions. This concept hinges on dynamically manipulating pixel distributions while maintaining image integrity, offering adaptability and versatility across various image types and contexts. UCEV’s automated approach eliminates the need for manual parameter adjustment, optimizing image quality by achieving a balance between contrast enhancement and preservation of image details through interaction with entropy maximization, resulting in enhanced visual interpretation and improved image quality.UCEV introduces an adaptive mechanism, denoted as wing Eq. .8\documeJ45 is maximized concerning A global measurement, entropy  the Eq.  for measWithin the analyzed colour space, the expansion of the pixel distribution is curtailed. In this study, the repositioned pixel points that exceed the boundaries of the colour space are constrained to remain within those limits. As a consequence, the escalation in image entropy ceases once specific pixel points converge upon the boundary edges. A higher value of entclass1pt{minimaAn insightful observation has been presented, highlighting the significance of determining the optimal UCEV value , which was performed between the original and enhanced images. The Eq.  shows thAnother method known as Root-Mean-Square Contrast (RMSC) is used for quantifying the contrast enhancement process, where the difference between each pixel point is calculated as shown in the Eq. .11\documSpatial distribution of contrast does not rely on the measurement of the RMSC, which means that it cannot provide the existence of artifacts. This method provides an overall impression of the image feature and its background. An image with a higher RMSC is desirable, but if its value is too high, it will be over-enhanced.YOLOv5 is a single-stage object detector containing three crucial components like another single-stage detector. The components of YOLOv5 are the model backbone, model neck, and model head. Model backbone is the initial stage, whose primary purpose is to extract significant features from an input image. In YOLOv5, the cross-stage partial networks (CSP) are applied as a backbone to extract rich, informative features from an input image. With deeper networks, CSPNet has demonstrated a considerable reduction in processing time.YOLOv5 has a second component known as the model neck, which generates feature pyramids. Feature pyramids assist models in developing decent object scaling generalizations. Model neck helps to recognize the same object in various sizes and scales. Models that employ feature pyramids perform well on unseen data. Different feature pyramid methods are available such as BiFPN, PANet, FPN, etc. PANet is a feature pyramid technique that is used in YOLOv5. The model head performs the final detection part. Anchor boxes are applied to features through which final output vectors are generated along with class probabilities, objectness, and bounding boxes. The model head is the same as in previous YOLO versions. The final model architecture for YOLOv5 is shown in Fig. YOLOv5 was used to recognize the yeast cells in the investigated images. YOLOv5 is an object detection method based on CNN structure. The confidence level of each recognized image pattern based on CNN is evaluated, and the optimal bounding box of a recognized image pattern is then determined. The yeast cell image patterns learning database in this paper is built on the tool Labellmg and Roboflow.To comprehensively investigate the impact of contrast enhancement in automatic yeast cell detection, our study meticulously created two sets of image databases. The first encompassed the original raw yeast cell images, while the second was composed of meticulously enhanced images utilizing the FACE approach. This dual-pronged approach enabled us to intricately probe the effect of contrast enhancement on the efficacy of the yeast cell detection methodology, seamlessly integrated with the cutting-edge YOLOv5 framework. The process of automatic cell detection using this FACE-YOLO model was shown by a schematic diagram shown in Fig. The yeast cell detection process commences with the meticulous labelling and organization of the dataset. To facilitate this, two distinct datasets are meticulously curated: the initial dataset comprising raw cell images and an additional dataset comprising images of yeast cells subjected to enhancement via the FACE methodology. Subsequently, the labelled training images are allocated to the backbone, neck, and head of the architecture, culminating in the generation of the desired output. The weights acquired through the training process are then applied to the testing phase, yielding conclusive results. Detection is executed through systematic manipulation of the input images and datasets. Initially, the raw training dataset is employed for cell detection, involving both raw images and FACE-enhanced images. Following this, the training dataset enriched with FACE-enhanced images is deployed for cell detection, encompassing both the original raw images and their enhanced counterparts. The computational parameter used for the FACE-YOLO model is detailed in Table A comparative analysis presented in Table For the task of finding the real-time position of the cell, the primary objective is to get the contour and pinpoint the precise coordinates of the yeast cell. This dynamic real-time process for detecting yeast cell contour and centre of the cell is illustrated in Fig. The microscopic images of yeast cells are taken from the laboratory, creating a database of 1000 raw images. This paper focuses on the performance of fuzzy clustering at The qualitative index was measured using RMSD and RMSC values for original microscopic and enhanced images, shown in Table While comparing the RMSD values, Table Next, YOLOv5 was used to recognize the yeast cells from the original microscopic images. In this study, the two models using a training dataset of the yeast cell image patterns are generated, and the effect of image enhancement on object detection by YOLOv5 is analyzed. The first model using the dataset was original raw yeast cell images, and the second model the dataset of enhanced images by FACE. Each model uses a different dataset to build a YOLO object detection system. When the performance compared for the detection of yeast cells between the original raw image model and the FACE model, it is observed that when the original raw image model is considered for YOLO detection, many cells are not identified due to being small in nature or blurred image, as shown in Fig. The performance of the FACE training model was analyzed using tenfold cross-validation accuracy and confidence score. As we can see, in the first fold and the second fold of the accuracy, the detection of the FACE training model had an accuracy above 90%, as compared with the detection of the raw image training model accuracy the FACE training model is more suitable for the blurry image or boundary of the cells are not clear. The confidence scores of the FACE training model are much higher than the raw training model, although the third and the fifth fold are lower, and these two confidence scores do not differ much. So the confidence scores of the FACE model are nearly the highest among the two models. The tenfold cross-validation accuracy and confidence scores are shown in Table The image shown in Fig. 46 representing all the object classes. It is noted that the boundaries of the yeast cells in the enhanced images are more precise and accessible to recognize than those in the original images. At first, the model is trained with 100% raw images to set a reference result. Then the next model is trained repeatedly, and for each new session, 10% of the dataset is switched out to enhanced images until the training dataset is 100% enhanced images. The same was done with the synthetic dataset. Then the following models are trained with different enhanced: synthetic dataset ratios. For determining the quality of the results, we used metrics such as mAP_0.5. The recognition accuracy demonstrated by the dark blue curve, which is found to be low, was based on the original microscopic images of yeast cells. When the accuracy is performed using 40% synthetic, and 60% enhanced images, it provides good detection accuracy, shown by the curve in grey color. The other curves are based on different research settings and can be seen in Fig. The recognition accuracy is demonstrated using different raw, enhanced, and synthetic training datasets. The synthetic dataset is a computer-generated yeast cell image from Blender3DIn the pursuit of enhancing the efficiency of the proposed model, a comparative study was undertaken involving two state-of-the-art deep learning models: FACE-YOLO and Mask R-CNN, for the task of automated yeast cell detection. The tenfold accuracy results of both models are presented in Table This work proposes Fuzzy Automatic Contrast Enhancement (FACE) as an image quality enhancement technique to enhance the microscopic images of yeast cells. FACE employs fuzzy clustering to prevent visible pixel separation, while a universal contrast enhancement variable (UCEV) optimizes entropy for enhanced image quality. In this work, the image enhancement of the microscopic image of yeast cells was done automatically without any feed of human-defined parameters. Finally, FACE created a less congested image pixel distribution with a significantly larger picture contrast while effectively enhancing the yeast cell image’s contrast level without producing local flaws such as a change in colour, visual artifacts, and over-strengthening. When the qualitative index was performed, and compared to other methods, FACE performed better enhancement results. The integration of YOLOv5 for automatic yeast cell detection further elevates the study's contributions. Notably, the FACE-enhanced model within YOLOv5 improves yeast cell detection accuracy. Impressively, the FACE-YOLO model outperforms Mask R-CNN with a remarkable 94.2% accuracy. This study underscores the promising synergy between FACE-enhanced imagery and advanced detection techniques, offering a pathway for heightened precision and effectiveness in cell analysis across a spectrum of biomedical applications. Additionally, the localization of cell boundaries and centres through OpenCV enhances cell positioning accuracy. As a future direction, integrating the automatic FACE-YOLO cell detection with a microgripper robot holds the potential for facilitating cell manipulation and handling.


Prescription opioid abuse is associated with substantial economic burden, with estimates of incremental annual per-patient health care costs of diagnosed opioid abuse exceeding $10,000 in prior literature. A subset of patients diagnosed with opioid abuse has disproportionately high health care costs, but little is known about the characteristics of these patients.To describe the characteristics of a subset of patients diagnosed with opioid abuse with disproportionately high health care costs to assist physicians and managed care organizations in targeting interventions at the costliest patients.This retrospective claims data analysis identified patients aged 12 to 64 years diagnosed with opioid abuse/dependence in the OptumHealth Reporting and Insights medical and pharmacy claims database, Quarter 1 (Q1) 1999-Q1 2012. Inclusion criteria required that patients had a diagnosis of opioid abuse during or after Q1 2006, no prior diagnoses of opioid abuse, and continuous non-HMO coverage over an 18-month study period. The study period comprised a 12-month observation period centered on the date of the first opioid abuse diagnosis (index date) and a 6-month baseline period immediately preceding the observation period. Patients in the top 20% of total health care costs in the observation period were classified as “high-cost patients,” and the remaining patients were classified as “lower-cost patients.” Patient characteristics, comorbidities, health care resource use, and health care costs were compared between high-cost patients and lower-cost patients using chi-square tests for dichotomous variables and Wilcoxon rank-sum tests for continuous variables. In addition, multivariate regression was used to assess the relationship between patient characteristics in the baseline period and total health care costs in the observation period among all patients diagnosed with opioid abuse.P < 0.001) and more likely to be female . They had a higher comorbidity burden at baseline, as reflected in the Charlson Comorbidity Index , and rates of conditions such as chronic pulmonary disease  and mild/moderate diabetes . High-cost patients also had higher rates of nonopioid substance abuse diagnoses  and psychotic disorders . In the observation period, high-cost patients continued to have higher rates of nonopioid substance abuse diagnoses  and psychotic disorders . In addition, they had greater medical resource use across all places of service  compared with lower-cost patients. The mean observation period health care costs of high-cost patients was $89,177 compared with $11,653 for lower-cost patients (P < 0.001). High-cost patients had higher medical costs linked to claims with an opioid abuse diagnosis in absolute terms, but the share of total medical costs attributed to such claims was lower among high-cost patients than among lower-cost patients. While many baseline characteristics were found to have a statistically significant (P < 0.05) association with observation period health care costs, only 27.3% of the variation in observation period health care costs was explained by patient characteristics in the baseline period.9,291 patients diagnosed with opioid abuse met the inclusion criteria. The 20% of patients classified as high-cost patients accounted for approximately two thirds of the total health care costs of patients diagnosed with opioid abuse. Compared with lower-cost patients, high-cost patients were older  can explain the wide variation in health care costs among opioid abusers. What is already known about this subjectPrescription opioid abuse is associated with substantial health and economic burden in the United States.The prevalence of diagnosed opioid abuse increased from 6.7 per 10,000 in 2006 to 18.6 per 10,000 in 2011 in a commercially insured population.Existing evidence suggests that patients diagnosed with opioid abuse incur higher health care resource use and costs than patients not diagnosed with opioid abuse.What this study addsThe 20% of patients with the highest costs accounted for approximately two thirds of the total health care costs of patients diagnosed with opioid abuse.High-cost patients had higher rates of chronic comorbidities and mental health conditions than did lower-cost patients, highlighting the need to consider the complete medical and psychosocial history of a patient when managing underlying conditions that contribute to higher health care costs.Much of the variation in health care costs cannot be attributed to comorbidities, however.1 Rates of diagnosed opioid abuse/dependence (known hereafter as “opioid abuse”) have grown over time. In a recent study using commercial claims data, Rice et al. (2014) reported that the prevalence of diagnosed opioid abuse increased from 6.7 per 10,000 in 2006 to 18.6 per 10,000 in 2011.2 Other studies using administrative claims data have found increasing rates of diagnosed opioid abuse among commercially insured populations as well as among publicly insured populations such as those covered by Medicaid, Medicare, and the Veteran’s Health Administration.5The abuse of prescription opioids is a major public health problem in the United States. The 2012 National Survey on Drug Use and Health (NSDUH) reported that an estimated 2.1 million Americans aged 12 years and older (or 0.8% of that population) had prescription pain reliever dependence or abuse.6 it remains to be seen whether this change in trend has also occurred in broader patient populations or whether it is specific to the types of patients who entered these RADARS programs during this time period. Nevertheless, the public health burden of opioid abuse remains substantial. The Drug Abuse Warning Network reported that there were approximately 366,000 emergency department (ED) visits in 2011 associated with nonmedical use of narcotic pain relievers, out of over 5 million ED visits that were drug related.7While a recent study using Researched Abuse, Diversion, and Addiction-Related Surveillance (RADARS) data from drug-diversion investigators, poison centers, substance abuse treatment centers, and college students reported declining rates of diversion and abuse among individuals in 5 RADARS programs starting around 2011,P < 0.01).2 A follow-up study by Rice et al. (2014) using a different and larger commercial claims database yielded similar results; excess annual per-patient health care costs of diagnosed opioid abuse were estimated to be $11,376 .8 In a recent study of the excess medical costs of diagnosed opioid abusers based on administrative claims data during 2009-2012, Rossiter et al. (2014) estimated excess medical costs of diagnosed opioid abusers of $9,456 for commercially insured continuous users of extended-release/long-acting opioids.9 White et al. (2011), using a demographically matched sample of opioid abusers and nonabusers, estimated excess annual health care costs of diagnosed opioid abuse of $20,546 per commercially insured patient.3 At a societal level, Birnbaum et al. (2011) estimated that the total societal costs of prescription opioid abuse, including direct costs and indirect costs  were $55.7 billion in 2007.10In addition, prior research has identified substantial opioidabuse-related costs to payers and society. Rice et al. matched commercially insured diagnosed opioid abusers to controls  based on baseline demographics, comorbidities, and health care resource use and estimated excess annual per-patient health care costs of diagnosed opioid abuse of $10,627 .Many of these findings reflect average excess health care costs among patients diagnosed with opioid abuse and do not capture considerable variation in the costs of opioid abuse. Health care spending in general  is quite concentrated, with patients in the top 20% in terms of health care spending accounting for over three quarters of total health care spending in 2009.International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] diagnosis codes, procedures performed, places of service, and payment amounts); pharmacy claims ; and eligibility information (patient demographics and enrollment history). The data were de-identified by the data vendor in order to be compliant with the Health Insurance Portability and Accountability Act of 1996. Therefore, institutional review board approval was not necessary for this study. Data for services provided covering the period of Quarter 1 (Q1) 1999 through Q1 2012 were used in the analyses that follow.Patients with commercial health insurance coverage were selected from the OptumHealth Reporting and Insights  medical and pharmacy claims database. The OptumHealth database currently contains approximately 18.5 million commercially insured beneficiaries  of 84 large self-insured companies with locations across the United States and representing a variety of industries and job types. The data include medical claims  coverage throughout the 18-month study period  to ensure that all relevant medical and pharmacy claims were captured for the final sample of patients. Patients as young as aged 12 years were included because opioid abuse is a concern among youth aged 12-17 years, and they have been included in prior studies on opioid abuse.Among all patients diagnosed with opioid abuse included in the study cohort, “high-cost patients” were defined as patients with total health care costs in the top quintile  of total health care costs among the study cohort during the 12-month observation period . The rem18 The Charlson Comorbidity Index is a method of categorizing patient comorbidities based on ICD-9-CM diagnosis codes, and each comorbidity category is assigned a weight from 1 to 6 based on the adjusted risk of mortality. The sum of all the weights results in a single score for each patient, and a higher risk of mortality corresponds with a higher score. Individual categories of the Charlson Comorbidity Index, such as chronic pulmonary disease, diabetes, and rheumatologic disease, were also tabulated. In addition, given their relevance to patients diagnosed with opioid abuse, rates of nonopioid substance abuse diagnoses, psychotic disorders, and other mental disorders were calculated as well. Comorbidities were identified using ICD-9-CM diagnosis codes  and the year of the index date . In addition, baseline comorbidities were assessed. The Charlson Comorbidity Index was derived based on the Romano adaptation.The previously mentioned comorbidities were also examined during the 12-month observation period. In addition, health care resource use and health care costs were examined. Health care resource use included the rate of any health care resource use and the intensity of such use , and the measures were medical resource use by places of service and prescription drug use. Medical claims were classified into places of service  using codes for place of service, provider specialty, provider type, and type of service codes (available upon request). Claims not classified in 1 of these 4 categories were classified as “other facility.”Health care costs were calculated as the total amount paid to service providers by the insurer for all covered services, regardless of diagnosis, as reflected on the claims. Out-of-pocket payments by patients  were not included. Measures included total health care costs, total medical costs, medical costs by places of service, and prescription drug costs. Health care costs were inflated to 2012 U.S. dollars using the medical care component of the Consumer Price Index.The share of medical resource use directly attributed to opioid abuse (vs. other causes) was also examined. “Abuse-related medical resource use” was defined as medical resource use linked to claims with opioid abuse diagnoses. Note that this likely reflects a lower-bound estimate of opioid abuse-related medical resource use, since some medical care triggered by opioid abuse may not be coded as related to opioid abuse for insurance claims purposes. Medical resource use not linked to claims with opioid abuse diagnoses was defined as “other medical resource use.” “Abuse-related medical costs” and “other medical costs” were similarly defined.Dichotomous variables were summarized using frequencies and percentages. Continuous variables were summarized using means and standard deviations. Comparisons between high-cost patients and lower-cost patients diagnosed with opioid abuse were conducted using chi-square tests for dichotomous variables and Wilcoxon rank-sum tests for continuous variables.P < 0.05.In addition, a multivariate regression analysis was conducted to examine the relationship between patient characteristics in the baseline period and health care costs in the observation period among all patients diagnosed with opioid abuse. Specifically, we estimated an ordinary least squares (OLS) regression model in which 12-month observation period health care costs (log-transformed) was the dependent variable. Health care costs were log-transformed to account for their skewed distribution; log-transformed costs were approximately normally distributed  and more likely to be female , as well as specific conditions such as chronic pulmonary disease , mild/moderate diabetes , rheumatologic disease , congestive heart failure , and malignancies . High-cost patients also had increased rates of nonopioid substance abuse diagnoses , psychotic disorders , and other mental disorders .Relative to lower-cost patients, high-cost patients were older , as well as specific conditions such as chronic pulmonary disease , mild/moderate diabetes , rheumatologic disease , congestive heart failure , and malignancies , psychotic disorders , and other mental disorders .In the observation period, high-cost patients continued to have increased rates of comorbidities, as reflected by the Charlson Comorbidity Index  and at least 1 ED visit . They also had longer inpatient stays  and spent more days in the ED . High-cost patients were more likely to have any prescription drug fill , and they filled more prescriptions  and more unique prescriptions as measured by different National Drug Code numbers  compared with lower-cost patients.Compared with lower-cost patients, high-cost patients had a greater likelihood of utilizing medical resources across all places of service and did so with greater intensity. For example, high-cost patients were more likely to have at least 1 inpatient visit , ED , and drug/alcohol rehabilitation facility . However, the share of overall all-cause medical resource use that was related to opioid abuse was lower among high-cost patients than among lower-cost patients across all places of service. For example, opioid abuse-related inpatient days accounted for 29% of inpatient days for high-cost patients but accounted for 56% of inpatient days for lower-cost patients. Abuse-related ED days accounted for 15% of ED days for high-cost patients but accounted for 25% of ED days for lower-cost patients.High-cost patients also had a greater intensity of opioid abuse-related medical resource use in the inpatient setting , of which 38.8%  was attributed to inpatient costs, 21.9%  to outpatient costs, 18.6%  to ED costs, 4.9%  to drug/alcohol rehabilitation facility costs, 4.8%  to other facility costs, and 11.0%  to prescription drugs. In total, high-cost patients accounted for approximately two thirds of the total health care costs of patients diagnosed with opioid abuse  and opioid abuse-related medical costs that were 4.5 times higher  than lower-cost patients. Although high-cost patients had higher health care costs across all places of service, the largest differential was due to inpatient stays . In addition, high-cost patients had higher prescription drug costs compared with lower-cost patients .High-cost patients had overall all-cause medical costs that were nearly 9 times higher  were consistent with those using the full 12-month observation period (results available upon request).P < 0.05) relationship with observation period health care costs when considered jointly in a multivariate regression framework . In addition, nonopioid poisoning and psychiatric illnesses were 78.0 and 8.5 times more prevalent among opioid abusers than among nonabusers, respectively (P < 0.01). Becker et al. (2008) also found that mental health conditions  were prevalent among opioid abusers than among nonabusers.20 Furthermore, 2 review articles also had similar observations. Jan (2010) noted that psychiatric disorders are common among patients with opioid dependence.21 Strassels (2009) noted that patients who abuse opioids may be at risk for certain conditions such as hepatitis or human immunodeficiency virus, as they may be at higher risk for hazardous routes of administration such as intravenous use.22This study provides insights into the challenges physicians and managed care organizations face in managing patients diagnosed with opioid abuse. Compared with lower-cost patients, the costliest patients diagnosed with opioid abuse were found to have higher rates of preexisting and concurrent chronic comorbidities and mental health conditions. While we focused on identifying differences within the population of diagnosed opioid abusers, these findings are consistent with prior research on opioid abuse comparing opioid abusers to nonabusers. White et al. (2005) reported that opioid abusers have higher rates of certain comorbidities than do nonabusers.Reducing costs and improving quality and outcomes are important functions of managed care organizations. With nearly two thirds of the health care costs associated with patients diagnosed with opioid abuse concentrated in just one fifth of patients, being able to target the subset of patients that accounts for a disproportionate share of the economic burden of opioid use is an important step towards achieving these goals. High-cost patients had greater all-cause and opioid abuse-related medical resource use and costs in absolute terms, so addressing opioid abuse in this population could reduce the overall economic burden of opioid abuse by reducing the medical costs of the costliest opioid abuse patients. However, the finding that the share of overall all-cause medical resource use and costs that was directly related to opioid abuse was lower among high-cost patients than among lower-cost patients suggests that high-cost patients would likely continue to have high medical resource use and costs due to other conditions. High-cost patients have higher rates of nonopioid abuse-related comorbidities, so the share of total health care costs attributed to opioid abuse specifically is lower than that among lower-cost patients who have lower rates of nonopioid abuse-related comorbidities. In addition, differences in rates of comorbidities between high-cost patients and lower-cost patients widened in the observation period, suggesting that disease progression for nonopioid abuse-related comorbidities may have been more pronounced or accelerated among high-cost patients. Better understanding of the characteristics of high-cost patients diagnosed with opioid abuse could help physicians and managed care organizations target their efforts to address opioid abuse and to reduce health care costs.This study found that high-cost patients were complicated patients with higher rates of baseline comorbidities. Opioid abuse may interact with these baseline comorbidities and make it difficult for patients to adhere to treatment plans for underlying conditions, thereby resulting in greater health care resource use and costs. This suggests that there is a need for greater awareness and screening of comorbid conditions when patients are first diagnosed with opioid abuse. Baseline comorbidities, combined with baseline health care costs and resource use, explained a relatively small share of the variation in observation period health care costs, however.23Using administrative claims data, it would be difficult to predict which opioid abusers will be the costliest patients based on characteristics prior to the first opioid abuse diagnosis. Therefore, future research should explore other reasons for the disproportionately higher health care costs among high-cost patients, such as differences in the severity of opioid abuse, which cannot be observed in administrative claims data but could potentially be observed in electronic medical records or survey data, for example. A multifaceted approach may be necessary to address opioid abuse at the societal level. In addition to treatment for patients diagnosed with opioid abuse, other efforts may include education of physicians, risk stratification tools to identify patients at higher risk of opioid abuse, prescription drug monitoring programs, and new drug formulations intended to deter opioid abuse.This study has a number of limitations. First, the study did not assess the causal relationship between patient characteristics and health care costs among diagnosed opioid abusers. Moreover, no causal relationship was established between opioid abuse severity and costs, since data were not available regarding opioid abuse severity. Because much of the variation in observation period health care costs cannot be explained by observable patient characteristics, further research is necessary to understand the extent to which treating or preventing opioid abuse among the higher cost patients would reduce health care costs. Second, as with any claims-based study, this study relied on the accuracy of claims data, and any miscoding in the underlying data could affect our results. For example, opioid abusers were identified using ICD-9-CM diagnosis codes for opioid abuse, but a substantial share of opioid abusers may be undiagnosed. In addition, opioid abuse may be coded as another condition, such as depression, for reimbursement purposes. These undiagnosed opioid abusers would not be captured in this analysis. In addition, our analysis of opioid abuse-related medical resource use and opioid abuse-related medical costs relied on medical claims associated with ICD-9-CM diagnosis codes for opioid abuse. As previously noted, these claims likely reflect a lower-bound estimate of opioid abuse-related resource use and costs, since some medical care resulting from opioid abuse  may not be coded as opioid abuse-related for insurances claims purposes. Third, the rates of comorbidities estimated in this study are likely underestimates due to the relatively short duration of the baseline period . A longer baseline period would have allowed for the observation of more comorbidities. In addition, only the primary and secondary diagnosis codes are included in the OptumHealth claims data, so any additional diagnoses would not be captured in our analysis. Fourth, the OptumHealth claims data do not include race or socioeconomic status, which may affect opioid abuse and the costs associated with opioid abuse. Finally, this study used the OptumHealth commercial claims database. Future research should examine the characteristics of high-cost patients diagnosed with opioid abuse in Medicaid, Medicare, and uninsured populations.Addressing a problem as complicated as prescription opioid abuse requires a nuanced understanding of opioid abusers. Such an understanding could help physicians and managed care organizations identify opportunities for intervention and target their efforts to address prescription opioid abuse and reduce the associated costs. This study found that high-cost patients accounted for a disproportionately large share of the total health care costs of patients diagnosed with opioid abuse. The costliest patients diagnosed with opioid abuse had high rates of preexisting and concurrent chronic comorbidities and mental health conditions. These results highlight the need to consider the complete medical and psychosocial patient history to better manage the underlying conditions contributing to high health care costs. Abuse may also exacerbate existing conditions and make it difficult for patients to adhere to treatment plans. In addition to targeting the subset of patients that accounts for a disproportionate share of the economic burden of opioid abuse, other multifaceted efforts, such as provider education, coordinated programs that address medical and social needs, prescription drug monitoring programs, and new drug formulations intended to deter opioid abuse, may be necessary to address prescription opioid abuse at the societal level.


Handle these nerves with care,” and those words still echo, conveying a loud and clear message that, however rare, peripheral nerve injury (PNI) remains a perturbing possibility that cannot be ignored. The unprecedented nerve injuries associated with peripheral nerve blocks (PNBs) can be most tormenting for the unfortunate patient and a nightmare for the anesthetist. Possible justifications for the seemingly infrequent occurrences of PNB-related PNIs include a lack of documentation/reporting, improper aftercare, or associated legal implications. Although they make up only a small portion of medicolegal claims, they are sometimes difficult to defend. The most common allegations are attributed to insufficient informed consent; preventable damage to a nerve(s); delay in diagnosis, referral, or treatment; misdiagnosis, and inappropriate treatment and follow-up care. Also, sufficient prospective studies or randomized trials have not been conducted, as exploring such nerve injuries (PNB-related) in living patients or volunteers may be impractical or unethical.Selander emphatically said, “Understanding the pathophysiology of various types of nerve injury is vital to dealing with them further. Processes like degeneration, regeneration, remyelination, and reinnervation can influence the findings of electrophysiological studies. Events occurring in such a process and their impact during the assessment determine the prognosis and the need for further interventions. This educational review describes various types of PNB-related nerve injuries and their associated pathophysiology. Postoperative neurological symptoms due to nerve injuries can be present in up to 15% of patients ,3. ApartThe incidence of PNB-related transient neuropathies ranges from 0-2.2% at three months, 0-0.8% at six months, and 0-0.2% at one year . SimilarNerve injuries can occur more frequently in the upper extremities than in the lower extremities. The most injured nerve is the radial nerve in the upper limb, followed by the median and ulnar nerves, and the sciatic nerve in the lower limbs, followed by the peroneal and tibial nerves . It can This article is part of a comprehensive overview of the essential understanding of peripheral nerves before blocking them. This educational overview will help readers understand the types, associated risk factors, mechanisms, and pathophysiology of nerve injuries that ultimately affect the overall outcomes and prognosis.This narrative overview describes the various types, associated risk factors, mechanisms, and pathophysiology of nerve injuries. Related literature searches were performed using online platforms  using relevant search terms (neurons/nerve damage/nerve injuries/pathophysiology of nerve injury/nerve injury mechanisms/nerve injury risk factors/nerve degeneration/nerve regeneration/reinnervation). Articles published in English were selected, and their reference sections were manually searched for additional information.Legalities of iatrogenic nerve injuriesIatrogenic  nerve injuries are always a cause for concern as they are caused by the treating physician, related personnel, or circumstances during the treatment process. When a patient comes into the hospital with no nerve damage and comes out with nerve damage, a charge of negligence can seldom be resisted . AlthougThe most common allegations are attributed to insufficient informed consent; preventable damage to a nerve or nerves; delay in diagnosis, referral, or treatment; and misdiagnosis and inappropriate follow-up treatment . The essAnatomy and histopathology of peripheral nervesAny pathology affects the normal functional anatomy and physiological processes of the structure. Therefore, it is essential to understand the normal functional anatomy and physiology of the nerve before knowing the pathogenesis of nerve injuries. Nerve anatomy and physiology have already been described in Part A of this review article. As mentioned in that part, each nerve has neural and non-neural components Figure . Neural Risk factors contributing to nerve injuryThe potential etiologies causing nerve injury or contributing factors are categorized into patient-related, surgical-related, anesthesia-related, and perioperative conditions-related risk factors Figure  14-16].-16.14-16Various preexisting conditions and comorbid factors can cause damage to single nerves (mononeuropathy) or many nerves (polyneuropathy). Depending on the number of nerves involved, the type of neurons affected, and the processes involved, peripheral neuropathies are classified in many ways Figure  17-19].-19.17-19Diabetic neuropathyDiabetic peripheral neuropathy (DPN) is the most common complication of diabetes Figure  21,22].,22.21,22Schwann cells are responsible for neuronal growth and regeneration by maintaining neuronal structure and function, nourishing axons, and promoting survival and repair after injury . SchwannThyroid neuropathyThyroid hormones significantly affect normal tissue growth, metabolism, and development. They regulate many functions and processes of the nervous system and exert various effects on the neuromuscular system and the brain . TherefoThyroid dysfunction and DMDM and thyroid dysfunction are common endocrinopathies in adults that always interact and frequently coexist Figure . The patStudies have shown a very high prevalence of thyroid dysfunction in type 2 DM. Therefore, routine determination of thyroid hormone levels is recommended in diabetics whose conditions are difficult to treat. Both hyperthyroidism and hypothyroidism carry the risk of glucose intolerance . Nearly Double crush syndromeThe presence of an underlying chronic neuropathy resulting from mechanical (compression), ischemic , toxic (cisplatin chemotherapy), or metabolic (diabetes) disease may predispose patients to further neurological injury . Such a Triple crush syndromeTriple crush/triple hit injury or syndrome is rarely described in a few anecdotal reports. Like double crush syndrome described for conditions like carpal tunnel/ cubital tunnel syndrome, triple crush syndrome incidences have been reported in a few cases where nerve insults occur at three different sites. These cases include diabetic polyneuropathy  , cervicaProposed mechanisms of pathophysiology of triple crush syndrome include impairment of axonal transport, up- and down-regulation of ion channels, and inflammatory changes in the dorsal root ganglion . The volRelevance to the practice of RADiabetic patients are at least twice as likely to require surgery than non-diabetics because of their comorbidities and the type of surgery performed . They arThe higher stimulating current requirement in diabetics can be due to decreased nerve conduction velocity, action potential amplitude, and excitability. Involved mechanisms include mitochondrial oxidative dysfunction, oxidative stress, inflammation, decreased Na-K-ATPase activity, loss of myelinated fibers, and demyelination . These aTherefore, the choice of stimulating current can be particularly important when dealing with a patient with preexisting neurological deficits (like diabetic or thyroid neuropathy). The intensity and the duration of the current required for nerve stimulation are increased. Hence a setting in the PNS (amplitude and pulse width) has to be modified accordingly. An experienced anesthetist should do a nerve block when performed. Landmark-based or paresthesia-based techniques are not recommended. Such neuropathic conditions can pose a high risk of intraneural needle placement and subsequent nerve damage when PNS is used alone. Therefore, techniques like dual guidance (USG +PNS) or triple guidance (USG + PNS + injection pressure manometer) can be better options to prevent nerve injuries and provide safe RA. Details of these techniques are included in the next part (part C) of this review article .Types of nerve injuriesNerve injury classification depends on the affected neural components, functional disruption, and neural recovery potential. In 1942, Seddon (a Professor of Orthopaedics) classified peripheral nerve injuries in English as a transient block, lesion in continuity, and division of nerve. Seddon classification is based on the degree of functional disruption ,82. His Similarly, in 1988, Lundborg (a surgeon)  classifiNeuropraxia (Transient Block)Neurapraxia is the mildest type of nerve injury associated with reversible conduction blockade due to localized myelin sheath degeneration or segmented demyelination. Such injury can be seen due to nerve compression leading to transient ischemic damage to the myelin sheath without affecting the axon and neural tube/connective tissues. Neuropraxic injury secondary to transient or intermittent ischemia improves quicker than injury secondary to focal demyelination. It predominately affects large nerve fibers more than small fibers . Focal dPerioperative neurapraxia occurs mainly due to improper patient positioning or poor padding of the pressure points leading to entrapment neuropathies . Such compression injuries can involve the ulnar nerve (at the elbow), radial nerve (at humerus), sciatic nerve (lithotomy position/hyperflexion of hip), or peroneal nerve (against the fibular head). LA can also cause neuropraxic injury by causing vasoconstriction, direct neurotoxic effect, or mechanical pressure effect due to LA administration. The inflammatory reactions during PNB can also cause scar formation and subsequent nerve entrapment. Other mechanisms include crush, traction, ischemia, thermal, electric shock, radiation, percussion, and vibration ,89. DetaIn neurapraxia, compound muscle action potential (CMAP) and sensory nerve action potential (SNAP) can be elicited distal to the injury. However, due to partial or complete conduction block proximal to the injury, varying degrees of decreased CMAP and SNAP amplitudes and decreased conduction velocity can be recorded in nerve conduction studies (NCS). Such changes improve completely or partially upon completion of remyelination. Electromyography (EMG) does not reveal any abnormal spontaneous activities in the form of fibrillations or positive sharp waves. It also shows normal morphology and an absent or reduced number of motor unit potentials (MUPs) on voluntary contractions. The prognosis is good, with usually complete recovery within days to weeks (12 weeks) . DetailsAxonotmesis (Lesion in Continuity)Axonotmesis is a more severe type of nerve injury than neuropraxia. The complete interruption of axons in axonotmesis can cause conduction loss in the nerve fiber beyond the zone of injury. It involves axonal damage without affecting neural connective tissues . Such injury is common with crush and stretch injuries . It can Recovery in axonotmesis involves collateral sprouting and regeneration. Collateral sprouting occurs if less than 20-30% of the motor axons are involved . It takeNCS shows conduction loss due to demyelination in the distal segment (3-4 days after injury) and small or absent CMAP or SNAP due to axonal loss. EMG studies show spontaneous electrical activities in the form of fibrillation potentials or sharp waves (2-3 weeks after injury) due to axonal loss leading to muscle denervation. The denervated muscles can subsequently become atrophic. A good prognosis with almost complete recovery can be possible without surgery if the causative factors that lead to nerve damage are removed in time. Recovery takes weeks to months (up to 18 months), involving axonal regrowth at a rate of 1 mm/day or 1 inch/month .Neurotmesis (Division of Nerve)Neurotmesis is the most severe form of nerve injury associated with the complete transection of the nerve. It is often observed after massive trauma, sharp injuries, traction or avulsion injuries, and the injection of noxious drugs . It invoMechanisms of the nerve injuriesNerve injuries are multifactorial in origin. Various mechanisms Figure  can be rMechanical TraumaMechanical trauma can cause neuropraxic injury to the nerve through compression and entrapment, leading to ischemia. On the other hand, other severe forms of nerve injury due to stretch and lacerations can also be possible. High injection pressure can increase intrafascicular pressure, which can lead to ischemic nerve damage if exceeded. Mechanical nerve injury can also be possible due to direct needle trauma or intraneuronal injections. Penetration of any needle or subsequent injection into the nerve can inevitably lead to inflammation and cellular infiltration, regardless of whether the clinical injury occurs.Needle TraumaThe characteristic of the mechanical injury caused by the needle depends on the needle size and type, especially the angle at the needle’s tip (bevel angle). The effect of needle size also affects the severity of the injury - smaller needles 24 gauge) may lead to less nerve injury than larger needles (19 gauge) . Needle 4 gauge mIntraneuronal InjectionIntraneural needle placement Figure  can be eThe perineurial multilayered epithelial lining does not readily stretch to accommodate increased intrafascicular pressure. Such increased intrafascicular pressure upon injection may remain higher than capillary perfusion pressure leading to neural ischemia and inflammation . The mecChemical MechanismIt includes the toxicity of injected solution containing either LA alone or a combination of LA and adjuvant. All LA agents are known to have a neurotoxic effect on the nerve that can occur even at clinical concentration levels ,109. NeuVarious LA adjuvants have been used in RA with mixed success. These include alpha-2 adrenoceptor agonists (clonidine and dexmedetomidine), steroids (dexamethasone), anti-inflammatory drugs (parecoxib and lornoxicam), midazolam, ketamine, magnesium sulfate, and neostigmine. The concerns about the safety profile of these adjuvants due to their potential for neurotoxicity and neurological complications warrant further investigation . AdrenalLA neurotoxicity can be concentration-dependent. Therefore, low-concentration LA can be considered safe in clinical practice. The neuraxial use of 5% lignocaine has been discontinued due to the occurrence of cauda equina syndrome. The recommended concentration of bupivacaine is 0.5%, whereas bupivacaine 0.75% has been abandoned in Europe . EvidencVascular MechanismThe vascular mechanism includes factors that can cause ischemic nerve damage by impairing neuronal blood flow. Such factors include the needle or injectate, which may contain LA or adjuvants. The vascular systems in the peripheral nerve include extrinsic or epineurial blood vessels and intrinsic or endoneurial blood vessels connected by oblique trans-perineurial blood vessels. Damage to these blood vessels by needle can result in external or internal hematoma formation. The compressive effect of the hematoma and subsequent edema formation can lead to neural ischemia due to reduced neural blood flow.LA alone can also decrease the dose- and concentration-dependent neural blood flow. Various concentrations of levobupivacaine and ropivacaine can significantly reduce sciatic nerve blood flow in rats ,136. AdrIntraneural but extrafascicular injection of LA can cause altered perineurial permeability and fascicular edema. It can lead to fascicular compression and decreased neural perfusion. Whereas intraneuronal and intrafascicular injection can cause a prolonged increase in endoneurial pressure. It can result in endoneurial ischemia if the increased endoneurial pressure exceeds capillary perfusion pressure. The earliest response to nerve ischemia is depolarization and the generation of spontaneous activity that is symptomatically perceived as paresthesia. It can also cause loss of all sensations with the onset of limb ischemia. If the ischemic time is less than 2 hours, nerve function can return within 6 hours. If the ischemia lasts up to 3 hours, the nerve undergoes edema formation and a degeneration phase of 1-2 weeks, followed by a regeneration phase of up to 6 weeks .Pressure MechanismThe pressure mechanism indirectly involves reduced blood supply to the nerve due to increased pressure in the structure around the nerve. Such increased pressure can result from improperly padded pressure points during patient positioning, compressive lesions such as hematoma or edema around neural structures, and using tourniquets. The femoral, ulnar, peroneal, lateral cutaneous nerve of the thigh and the brachial plexus are at risk of compression due to over-extension or flexion and inadequate padding during prolonged surgery. Compressive hematomas are most common in patients treated with anticoagulants ,140. In Tourniquet neuropathy Figure  can occuInflammatory MechanismInflammation after PNB or surgery can also affect a nerve at the same or distant site, causing neurological deficits. Studies have reported signs of chronic inflammation, such as adhesions, fascia thickening, vascular changes, and scar tissue formation that cause compression neuropathy in PNB . Such inPSIN can be definitively diagnosed by a nerve biopsy. However, magnetic resonance imaging (MRI) can support diagnosis and, together with the clinical evidence, enable a preliminary diagnosis and therapy . MagnetiPathogenesis after nerve injuryA complex multi-cellular response occurs when an axon in the peripheral nervous system (PNS) is injured. Any injury to the nerve can lead to the interruption of axonal continuity (axotomy), dividing it into two cytoplasmic compartments: proximal and distal. The proximal portion of the axon that remains in continuity with the cell body (soma) is the proximal nerve stump. The detached portion of the axon from the soma is the distal segment. The cell body begins to express regeneration-associated genes. The proximal segment forms a growth cone moving toward the denervated target, and the distal segment degenerates. Such degeneration and regeneration occur in injured nerves and nonneuronal cells, including Schwann and immune cells.Any nerve injury follows the process of degeneration followed by regeneration Figure  164]. D. D164]. Primary DegenerationIt is also called retrograde degeneration. It proceeds for at least one internodal space and heavily depends on the degree of proximal insult. The cut end of the proximal segment gets retracted from the injury site, known as proximal segment dieback . MorpholSecondary DegenerationIt is also known as anterograde or Wallerian degeneration, which occurs in the distal segment. It involves degeneration and destruction by phagocytosis and lasts about four weeks . This biThe axonal discontinuity followed by axonal transport disruption due to nerve injury results in an unregulated influx of calcium ions . SubsequThe degeneration of the axon leads to demyelination Figure  due to tNeuronal DeathSome axotomized neurons survive but become atrophied due to the limited anabolic response, leading to neuronal dysfunction. In contrast, some axotomized neurons undergo regressive changes leading to neuronal death. Some neurons that cannot survive axonal damage succumb to what is referred to as reverse cell death . Cell deRegenerationThe neuronal regeneration process is impossible in the central nervous system . It can The proximal nerve segment forms multiple sprouts from each damaged axon with an amoeboid structure at the end known as a growth cone . The forSchwann cells also begin to proliferate in the distal nerve stump within the first 24 hours after injury and reach a peak of mitotic activity after about three days . After 3The total time for regeneration depends on the healing time and dieback of the proximal nerve segment, the navigation of growth cones through the lesion to the distal stump, and the distance traveled by regenerating axons to reach their target. Nerve regeneration proceeds at 3-4 mm/day after crush  and apprIn summary, in-depth knowledge of the types and pathophysiology of nerve injuries can help to understand the extent and significance of adopting preventive measures in the perioperative period. In addition, various mechanisms of nerve injury provide clues that help anticipate their possibilities and encourage vigilant monitoring at every step in the perioperative period. PNB-related PNIs can be linked with the needle type/design, LA type/volume/concentration, adjuvant, target site, or administration techniques. For ensuring safe RA, the goal should be to select the safest modality and appropriate LA solutions (as per requirements), understand the existing damages of the preexisting conditions, and remain extraneural or extrafascicular. Time-lapse of pathologic processes after injury  can help prognosticate nerve injuries and ascertain appropriate treatment protocols. However, current research on PNIs presents a challenge, as exploring such PNB-related nerve injuries in living patients or volunteers may be unfeasible or unethical. Additionally, the surgical outcomes of nerve repairs are still in the gray zone, warranting further research into newer modalities to achieve the desired functional outcomes.