Novel anthropometric indices for predicting type 2 diabetes mellitus.

BACKGROUND: This study aimed to compare anthropometric indices to predict type 2 diabetes mellitus (T2DM) among first-degree relatives of diabetic patients in the Iranian community. METHODS: In this study, information on 3483 first-degree relatives (FDRs) of diabetic patients was extracted from the database of the Endocrinology and Metabolism Research Center of Isfahan University of Medical Sciences. Overall, 2082 FDRs were included in the analyses. A logistic regression model was used to evaluate the association between anthropometric indices and the odds of having diabetes. Furthermore, a receiver operating characteristic (ROC) curve was applied to estimate the optimal cutoff point based on the sensitivity and specificity of each index. In addition, the indices were compared based on the area under the curve (AUC). RESULTS: The overall prevalence of diabetes was 15.3%. The optimal cutoff points for anthropometric measures among men were 25.09 for body mass index (BMI) (AUC = 0.573), 0.52 for waist-to-height ratio (WHtR) (AUC = 0.648), 0.91 for waist-to-hip ratio (WHR) (AUC = 0.654), 0.08 for a body shape index (ABSI) (AUC = 0.599), 3.92 for body roundness index (BRI) (AUC = 0.648), 27.27 for body adiposity index (BAI) (AUC = 0.590), and 8 for visceral adiposity index (VAI) (AUC = 0.596). The optimal cutoff points for anthropometric indices were 28.75 for BMI (AUC = 0.610), 0.55 for the WHtR (AUC = 0.685), 0.80 for the WHR (AUC = 0.687), 0.07 for the ABSI (AUC = 0.669), 4.34 for the BRI (AUC = 0.685), 39.95 for the BAI (AUC = 0.583), and 6.15 for the VAI (AUC = 0.658). The WHR, WHTR, and BRI were revealed to have fair AUC values and were relatively greater than the other indices for both men and women. Furthermore, in women, the ABSI and VAI also had fair AUCs. However, BMI and the BAI had the lowest AUC values among the indices in both sexes. CONCLUSION: The WHtR, BRI, VAI, and WHR outperformed other anthropometric indices in predicting T2DM in first-degree relatives (FDRs) of diabetic patients. However, further investigations in different populations may need to be implemented to justify their widespread adoption in clinical practice.

Pneumothorax during manned chamber operations: A summary of reported cases.

In-chamber pneumothorax has complicated medically remote professional diving operations, submarine escape training, management of decompression illness, and hospital-based provision of hyperbaric oxygen therapy. Attempts to avoid thoracotomy by combination of high oxygen partial pressure breathing (the concept of inherent unsaturation) and greatly slowed rates of chamber decompression proved successful on several occasions. When this delicate balance designed to prevent the intrapleural gas volume from expanding faster than it contracts proved futile, chest drains were inserted. The presence of pneumothorax was misdiagnosed or missed altogether with disturbing frequency, resulting in wide-ranging clinical consequences. One patient succumbed before the chamber had been fully decompressed. Another was able to ambulate unaided from the chamber before being diagnosed and managed conventionally. In between these two extremes, patients experienced varying degrees of clinical compromise, from respiratory distress to cardiopulmonary arrest, with successful resuscitation. Pneumothorax associated with manned chamber operations is commonly considered to develop while the patient is under pressure and manifests during ascent. However, published reports suggest that many were pre-existing prior to chamber entry. Risk factors included pulmonary barotrauma-induced cerebral arterial gas embolism, cardiopulmonary resuscitation, and medical or surgical procedures usually involving the lung. This latter category is of heightened importance to hyperbaric operations as an iatrogenically induced pneumothorax may take as long as 24 hours to be detected, perhaps long after a patient has been cleared for chamber exposure.

Development of a multiplex polymerase chain reaction assay for detection of hepatitis C virus, hepatitis B virus, and human immunodeficiency virus 1.

BACKGROUND: Hepatitis C virus (HCV), hepatitis B virus (HBV), and human immunodeficiency virus 1 (HIV-1) are the most epidemic blood-borne viruses, posing threats to human health and causing economic losses to nations for combating the infection transmission. The diagnostic methodologies that depend on the detection of viral nucleic acids are much more expensive, but they are more accurate than serological testing. AIM: To develop a rapid, cost-effective, and accurate diagnostic multiplex polymerase chain reaction (PCR) assay for simultaneous detection of HCV, HBV, and HIV-1. METHODS: The design of the proposed PCR assay targets the amplification of a short conserved region featured with a distinguishable melting profile and electrophoretic molecular weight inside each viral genome. Therefore, this diagnostic method will be appropriate for application in both conventional (combined with electrophoresis) and real-time PCR facilities. Confirmatory in silico investigations were conducted to prove the capability of the approached PCR assay to detect variants of each virus. Then, Egyptian isolates of each virus were subjected to the wet lab examination using the given diagnostic assay. RESULTS: The in silico investigations confirmed that the PCR primers can match many viral variants in a multiplex PCR assay. The wet lab experiment proved the efficiency of the assay in distinguishing each viral type through high-resolution melting analysis. Compared to related published assays, the proposed assay in the current study is more sensitive and competitive with many expensive PCR assays. CONCLUSION: This study provides a simple, cost-effective, and sensitive diagnostic PCR assay facilitating the detection of the most epidemic blood-borne viruses; this makes the proposed assay promising to be substitutive for the mistakable and cheap serological-based assays.

The efficacy of modified contrast-enhanced ultrasound Liver Imaging Reporting and Data System (CEUS LI-RADS) using Sonazoid in diagnosis of hepatocellular carcinoma: a systematic review and meta-analysis.

Background: The contrast-enhanced ultrasound Liver Imaging Reporting and Data System (CEUS LI-RADS) is an algorithm for the diagnosis of hepatocellular carcinoma (HCC) in high-risk populations. Previous studies have shown the algorithm to have high specificity and moderate sensitivity. Nevertheless, it is designated for utilization solely with blood pool contrast agents. Sonazoid, a contrast agent that combines blood pools and Kupffer cells properties, has recently gained approval for marketing in an increased number of countries. Enhanced sensitivity in diagnosing HCC may be achieved through the distinctive Kupffer phase (KP) exhibited by Sonazoid. Certain academics have suggested the modified CEUS LI-RADS using Sonazoid. The main criteria of mild and late (≥60 seconds) washout in CEUS LI-RADS LR-5 were replaced by KP (>10 minutes) defects as the primary criteria. The purpose of this research was to evaluate the effectiveness of the modified CEUS LI-RADS using Sonazoid in diagnosing HCC. Methods: Original studies on Sonazoid and CEUS LI-RADS were searched in the PubMed, Embase, Cochrane Library, and Web of Science databases until 13 July 2023, with no restrictions on language. We enrolled studies that applied Sonazoid for CEUS in patients at high risk of HCC and modified CEUS LI-RADS for the diagnosis of intrahepatic nodules. Meta-analyses, evaluations, case studies, correspondences, remarks, and summaries of conferences were excluded. Additionally, studies that fell outside the scope of this study and contained data on the same patients were also excluded. We evaluated the quality of research by employing the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. A bivariate mixed effects model was utilized to conduct a meta-analysis, summarizing the sensitivity and specificity in the diagnosis of HCC. The investigation of potential factors contributing to study heterogeneity was conducted using meta-regression analysis. Results: Out of the 103 studies screened, 6 studies (835 lesions) were included in the final results. Modified CEUS LR-5 exhibited a sensitivity of 0.77 [95% confidence interval (CI): 0.70-0.82; I2=71.98%; P=0.00] and a specificity of 0.88 (95% CI: 0.83-0.92; I2=0.00; P=0.47) for HCC diagnosis, with heterogeneity in sensitivity. The presence of heterogeneity in the study was found to have a significant association with factors such as the study design, the number of image reviewers, the proportion of cirrhosis, the proportion of other non-HCC malignancies (OM) cases, and the type of reference standard (P≤0.05). Conclusions: The modified CEUS LI-RADS LR-5 categorization demonstrates a reasonable level of sensitivity 0.77, but an insufficient level of specificity 0.88 when diagnosing HCC. KP defects cannot be used as a primary feature in the diagnosis of HCC by CEUS LI-RADS, perhaps as an ancillary feature.

White matter biomarker for predicting de novo Parkinson's disease using tract-based spatial statistics: a machine learning-based model.

Background: Parkinson's disease (PD) is an irreversible, chronic degenerative disease of the central nervous system, potentially associated with cerebral white matter (WM) lesions. Investigating the microstructural alterations within the WM in the early stages of PD can help to identify the disease early and enable intervention to reduce the associated serious threats to health. Methods: This study selected 227 cases from the Parkinson's Progression Markers Initiative (PPMI) database, including 152 de novo PD patients and 75 normal controls (NC). Whole-brain voxel analysis of the WM was performed using the tract-based spatial statistics (TBSS) method. The WM regions with statistically significant differences (P<0.05) between the PD and NC groups were identified and used as masks. The mask was applied to each case's fractional anisotropy (FA) image to extract voxel values as feature vectors. Geometric dimensionality reduction was then applied to eliminate redundant values in the feature vectors. Subsequently, the cases were randomly divided into a training group (158 cases, including 103 PD patients and 55 NC) and a test group (69 cases, including 49 PD patients and 20 NC). The least absolute shrinkage and selection operator (LASSO) regression algorithm was employed to extract the minimal set of relevant features, then the random forest (RF) algorithm was utilized for classification using 5-fold cross validation. The resulting model was further integrated with clinical factors to create a comprehensive prediction model. Results: In comparison to the NC group, the FA values in PD patients exhibited a statistically significant decrease (P<0.05), indicating the presence of widespread WM lesions across multiple brain regions. Moreover, the PD prediction model, constructed based on these WM lesion regions, yielded prediction accuracy (ACC) and area under the receiver operating characteristic (ROC) curve (AUC) values of 0.778 and 0.865 in the validation set, and 0.783 and 0.831 in the test set, respectively. Furthermore, the performance of the integrated model showed some improvement, with ACC and AUC values in the test set reaching 0.804 and 0.844, respectively. Conclusions: The quantitative calculation of WM lesion area on FA images using the TBSS method can serve as a neuroimaging biomarker for diagnosing and predicting early PD at the individual level. When integrated with clinical variables, the predictive performance improves.

Modeling of new markers for the diagnosis and prognosis of pancreatic cancer based on the transition from inflammation to cancer.

Background: Pancreatic adenocarcinoma (PAAD) is a lethal disease with a poor prognosis. Genes involved in acute pancreatitis (AP) or chronic pancreatitis (CP) might be important for PAAD development. This study sought to identify potential PAAD diagnosis markers and to establish a PAAD prognosis prediction model based on AP- and CP-related genes. Methods: The significantly differentially expressed genes in both AP or CP and PAAD were obtained by a bioinformatics analysis. A risk-score model for predicting survival was constructed based on The Cancer Genome Atlas (TCGA) data and validated using an International Cancer Genome Consortium (ICGC) cohort. Protein expression and the effects of the genes in the risk models were validated by immunohistochemistry, or Cell Counting Kit-8 (CCK-8) and transwell assays. The study sample data included six AP tissue samples and five normal pancreatic tissue samples, six CP tissue samples and six normal pancreatic tissue samples from the Gene Expression Omnibus (GEO) expression profiling microarrays GSE109227 and GSE41418 data sets, respectively, and fragments per kilobase per million mapped fragments (FPKM) data from four normal controls and 150 PAAD cases from TCGA database, and 182 cancer patient samples with complete survival prognostic data from the ICGC database. Results: In total, 508 significantly differentially expressed genes were found in both AP or CP and PAAD. Trefoil factor 2 (TFF2), tubulointerstitial nephritis antigen (TINAG), trefoil factor 1 (TFF1), aquaporin 5 (AQP5), SAM pointed domain containing ETS transcription factor (SPDEF), anterior gradient protein 2 (AGR2), apolipoprotein B messenger RNA editing enzyme catalytic subunit 1 (APOBEC1), kallikrein-related peptidase 6 (KLK6), dopa decarboxylase (DDC), mucin 13 (MUC13), claudin 18 (CLDN18), annexin A10 (ANXA10), and tetraspanin 1 (TSPAN1) were found to be present in PAAD and had the largest fold change. A risk-score model, comprising 19 genes, was constructed for prognostic prediction. A high-risk score indicated a poor prognosis. TINAG, DDC, SPDEF, and APOBEC1 proteins were increased in PAAD, while TINAG and DDC were correlated with the pathologic grade. Decreased TINAG, APOBEC1, transmembrane protein 94 (TMEM94), and kelch like family member 36 (KLHL36) expression inhibited PAAD cell proliferation, while decreased SPDEF, TMEM94, and KLHL36 expression significantly inhibited PAAD cell migration. Conclusions: The AP and CP co-related genes were significantly correlated with PAAD. TINAG, DDC, SPDEF, and APOBEC1 could serve as new PAAD predictors. The risk model developed in this study could be used to predict the prognosis of PAAD patients.

Enhanced lung cancer detection: Integrating improved random walker segmentation with artificial neural network and random forest classifier.

Background: Medical image segmentation is a vital yet difficult job because of the multimodality of the acquired images. It is difficult to locate the polluted area before it spreads. Methods: This research makes use of several machine learning tools, including an artificial neural network as well as a random forest classifier, to increase the system's reliability of pulmonary nodule classification. Anisotropic diffusion filtering is initially used to remove noise from a picture. After that, a modified random walk method is used to get the region of interest inside the lung parenchyma. Finally, the features corresponding to the consistency of the picture segments are extracted using texture-based feature extraction for pulmonary nodules. The final stage is to identify and classify the pulmonary nodules using a classifier algorithm. Results: The studies employ cross-validation to demonstrate the validity of the diagnosis framework. In this instance, the proposed method is tested using CT scan information provided by the Lung Image Database Consortium. A random forest classifier showed 99.6 percent accuracy rate for detecting lung cancer, compared to a artificial neural network's 94.8 percent accuracy rate. Conclusions: Due to this, current research is now primarily concerned with identifying lung nodules and classifying them as benign or malignant. The diagnostic potential of machine learning as well as image processing approaches are enormous for the categorization of lung cancer.

Application of intelligent tongue image analysis in Conjunction with microbiomes in the diagnosis of MAFLD.

Background: Metabolic associated fatty liver disease (MAFLD) is a widespread liver disease that can lead to liver fibrosis and cirrhosis. Therefore, it is essential to develop early diagnosic and screening methods. Methods: We performed a cross-sectional observational study. In this study, based on data from 92 patients with MAFLD and 74 healthy individuals, we observed the characteristics of tongue images, tongue coating and intestinal flora. A generative adversarial network was used to extract tongue image features, and 16S rRNA sequencing was performed using the tongue coating and intestinal flora. We then applied tongue image analysis technology combined with microbiome technology to obtain an MAFLD early screening model with higher accuracy. In addition, we compared different modelling methods, including Extreme Gradient Boosting (XGBoost), random forest, neural networks(MLP), stochastic gradient descent(SGD), and support vector machine(SVM). Results: The results show that tongue-coating Streptococcus and Rothia, intestinal Blautia, and Streptococcus are potential biomarkers for MAFLD. The diagnostic model jointly incorporating tongue image features, basic information (gender, age, BMI), and tongue coating marker flora (Streptococcus, Rothia), can have an accuracy of 96.39%, higher than the accuracy value except for bacteria. Conclusion: Combining computer-intelligent tongue diagnosis with microbiome technology enhances MAFLD diagnostic accuracy and provides a convenient early screening reference.

Quantitative analysis of mandibular cortical morphology using artificial intelligence-based computer assisted diagnosis for panoramic radiography on underlying diseases and dental status in women over 20 years of age.

Background/purpose: Recently, an artificial intelligence-based computer-assisted diagnosis (AI-CAD) for panoramic radiography was developed to scan the inferior margin of the mandible and automatically evaluate mandibular cortical morphology. The aim of this study was to analyze quantitatively the mandibular cortical morphology using the AI-CAD, especially focusing on underlying diseases and dental status in women over 20 years of age. Materials and methods: 419 patients in women over 20 years of age who underwent panoramic radiography were included in this study. The mandibular cortical morphology was analyzed with an AI-CAD that evaluated the degree of deformation of the mandibular inferior cortex (MIC) and mandibular cortical index (MCI) automatically. Those were analyzed in relation to underlying diseases, such as diabetes, hypertension, dyslipidemia, rheumatism and osteoporosis, and dental status, such as the number of teeth present in the maxilla and mandible. Results: The degree of deformation of MIC in women under 51 years of age (21-50 years; n = 229, 16.0 ± 12.7) was significantly lower than those of over 50 years of age (51-90 years; n = 190, 45.1 ± 23.0), and the MCI was a significant difference for the different age group. Regarding the degree of deformation of MIC and MCI in women over 50 years of age, osteoporosis and number of total teeth present in the maxilla and mandible were significant differences. Conclusion: The results of this study indicated that the mandibular cortical morphology using the AI-CAD is significantly related to osteoporosis and dental status in women over 50 years of age.

Decades Delayed in Diagnosis: Hidradenitis Suppurativa and a Review of Barriers to Care.

We present a case of a 40-year-old female seen on the inpatient general surgery service in consultation for a suspected abdominal wall abscess or seroma. The history and examination were consistent with a diagnosis of hidradenitis suppurativa. The patient had a 25-year history of similar lesions present since her teenage years, not properly investigated and diagnosed, despite presenting with symptoms in multiple clinic and hospital settings since disease onset. As an accurate diagnosis of HS is often missed or delayed for years, it is important to increase awareness and clinical recognition of this condition among providers to improve outcomes for patients with the potentially debilitating disease of HS.

Identification of a circulating three-miRNA panel for the diagnosis of primary open angle glaucoma.

PURPOSE: Conventional diagnosis of primary open angle glaucoma (POAG) needs a combination of ophthalmic examinations. An efficient assay is urgently needed for a timely POAG diagnosis. We aim to explore differential expressions of circulating microRNAs (miRNA) and provide novel miRNA biomarkers for POAG diagnosis. METHODS: A total of 180 POAG patients and 210 age-related cataract (ARC) patients were enrolled. We collected aqueous humor (AH) and plasma samples from the recruited patients. The expressions of candidate miRNAs were measured using quantitative real time polymerase chain reaction. The diagnostic ability of candidate miRNAs was analyzed by receiver operating characteristic curve. RESULTS: The expressions of miR-21-5p and miR-29b-3p were downregulated significantly in AH and plasma of POAG and miR-24-3p expression was significantly increased in AH and plasma of POAG, comparing with those of ARC. A three-miRNA panel was constructed by a binary logistic regression. And the panel could differentiate between POAG and ARC with an area under the curve of 0.8867 (sensitivity = 78.0%, specificity = 83.3%) in aqueous humor and 0.7547 (sensitivity = 73.8%, specificity = 81.2%) in plasma. Next, we verified the three-miRNA panel working as a potential diagnostic biomarker stable and reliable. At last, we identified related function and regulation pathways in vitro. CONCLUSIONS: In conclusion, we built and identified a circulating three-miRNA panel as a potential diagnostic biomarker for POAG. It may be developed into an efficient assay and help improve the POAG diagnosis in the future.

Human epididymal protein 4 and its combined detection show good diagnostic value in lung cancer: A retrospective study.

OBJECTIVES: This study aimed to assess the diagnostic value of human epididymal protein 4 (HE4), a potential novel biomarker for lung cancer, and its combined detection with five other conventional biomarkers in lung cancer diagnosis and subtyping. METHODS: In this retrospective study, 115 lung cancer patients, 50 patients with benign pulmonary disease, and 50 healthy controls were included. Serum HE4, progastrin-releasing peptide (ProGRP), squamous cell carcinoma (SCC) antigen, cytokeratin-19 fragment (CYFRA21-1), neuron-specific enolase (NSE), and carcinoembryonic antigen (CEA) were analyzed using the electrochemiluminescence immunoassay and chemiluminescence immunoassay. The receiver operating characteristic curve was performed to analyze the diagnostic efficacy of individual biomarkers in identifying both lung cancer and its histologic subtypes. RESULTS: All six biomarkers showed significantly elevated levels in the lung cancer group compared to both benign pulmonary disease and control groups (P < 0.05). Among the biomarkers evaluated, HE4 exhibited the highest diagnostic performance for lung cancer, lung adenocarcinoma, and lung squamous cell carcinoma with area under the curve (AUC) values of 0.921, 0.891, and 0.937, respectively. ProGRP was the optimal biomarker for small cell lung cancer with an AUC of 0.973. The combination of all six biomarkers yielded the largest AUCs in the diagnosis of lung cancer subtypes (0.937 for lung adenocarcinoma, 0.998 for lung squamous cell carcinoma, and 0.985 for small cell lung cancer). Furthermore, specific combinations, such as HE4 + CEA, HE4 + SCC, and ProGRP + HE4 + NSE, showed strong diagnostic performance in lung cancer. CONCLUSIONS: HE4 and its combined detection held substantial clinical significance in the diagnosis of lung cancer and its histologic subtyping, especially for lung adenocarcinoma and lung squamous cell carcinoma.

Complex cutaneous leishmaniasis in a pediatric patient.

Cutaneous leishmaniasis (CL), a parasitic infection caused by Leishmania protozoa and transmitted by sandfly bites, can be classified into Old World and New World subtypes. We report a case of a 2-year-old female who developed complex CL after travel to Panama. Ultimately, successful treatment required two rounds of liposomal amphotericin B. We report this case for its challenging clinical course and management.

Factors associated with the timely diagnosis of malaria and the utilization of types of healthcare facilities: a retrospective study in the Republic of Korea.

Objectives: This study aimed to analyze trends in the timely diagnosis of malaria cases over the past 10 years in relation to the utilization of different types of healthcare facilities. Methods: The study included 3,697 confirmed and suspected cases of malaria reported between January 1, 2013, and December 31, 2022, in the national integrative disease and healthcare management system. Some cases lacking a case report or with information missing from the case report were excluded from the analysis. A generalized linear model with a Poisson distribution was constructed to estimate rate ratios and 95% confidence intervals adjusted for other variables, such as distance. Results: When cases involving diagnosis >5 days after symptom onset in confirmed patients (5DD) were examined according to the type of healthcare facility, the rate ratio of 5DD cases was found to be higher for public health facilities than for tertiary hospitals. Specifically, the rate ratio was higher when the diagnosis was established at a tertiary hospital, even after a participant had visited primary or secondary hospitals. In an analysis adjusted for the distance to each participant's healthcare facility, the results did not differ substantially from the results of the crude analysis. Conclusion: It is imperative to improve the diagnostic capabilities of public facilities and raise awareness of malaria at primary healthcare facilities for effective prevention and control.

Artificial intelligence and machine learning responses to COVID-19 related inquiries.

Researchers and scientists can use computational-based models to turn linked data into useful information, aiding in disease diagnosis, examination, and viral containment due to recent artificial intelligence and machine learning breakthroughs. In this paper, we extensively study the role of artificial intelligence and machine learning in delivering efficient responses to the COVID-19 pandemic almost four years after its start. In this regard, we examine a large number of critical studies conducted by various academic and research communities from multiple disciplines, as well as practical implementations of artificial intelligence algorithms that suggest potential solutions in investigating different COVID-19 decision-making scenarios. We identify numerous areas where artificial intelligence and machine learning can impact this context, including diagnosis (using chest X-ray imaging and CT imaging), severity, tracking, treatment, and the drug industry. Furthermore, we analyse the dilemma's limits, restrictions, and hazards.

C. difficile biomarkers, pathogenicity and detection.

BACKGROUND: Clostridioides difficile infection (CDI) is the main etiologic agent of antibiotic-associated diarrhea. CDI contributes to gut inflammation and can lead to disruption of the intestinal epithelial barrier. Recently, the rate of CDI cases has been increased. Thus, early diagnosis of C. difficile is critical for controlling the infection and guiding efficacious therapy. APPROACH: A search strategy was set up using the terms C. difficile biomarkers and diagnosis. The found references were classified into two general categories; conventional and advanced methods. RESULTS: The pathogenicity and biomarkers of C. difficile, and the collection manners for CDI-suspected specimens were briefly explained. Then, the conventional CDI diagnostic methods were subtly compared in terms of duration, level of difficulty, sensitivity, advantages, and disadvantages. Thereafter, an extensive review of the various newly proposed techniques available for CDI detection was conducted including nucleic acid isothermal amplification-based methods, biosensors, and gene/single-molecule microarrays. Also, the detection mechanisms, pros and cons of these methods were highlighted and compared with each other. In addition, approximately complete information on FDA-approved platforms for CDI diagnosis was collected. CONCLUSION: To overcome the deficiencies of conventional methods, the potential of advanced methods for C. difficile diagnosis, their direction, perspective, and challenges ahead were discussed.

Managing Opioid Withdrawal Symptoms During the Fentanyl Crisis: A Review.

Illicitly manufactured fentanyl (IMF) is a significant contributor to the increasing rates of overdose-related deaths. Its high potency and lipophilicity can complicate opioid withdrawal syndromes (OWS) and the subsequent management of opioid use disorder (OUD). This scoping review aimed to collate the current OWS management of study populations seeking treatment for OWS and/or OUD directly from an unregulated opioid supply, such as IMF. Therefore, the focus was on therapeutic interventions published between January 2010 and November 2023, overlapping with the period of increasing IMF exposure. A health science librarian conducted a systematic search on November 13, 2023. A total of 426 studies were screened, and 173 studies were reviewed at the full-text level. Forty-nine studies met the inclusion criteria. Buprenorphine and naltrexone were included in most studies with the goal of transitioning to a long-acting injectable version. Various augmenting agents were tested (buspirone, memantine, suvorexant, gabapentin, and pregabalin); however, the liberal use of adjunctive medication and shortened timelines to initiation had the most consistently positive results. Outside of FDA-approved medications for OUD, lofexidine, gabapentin, and suvorexant have limited evidence for augmenting opioid agonist initiation. Trials often have low retention rates, particularly when opioid agonist washout is required. Neurostimulation strategies were promising; however, they were developed and studied early. Precipitated withdrawal is a concern; however, the rates were low and adequately mitigated or managed with low- or high-dose buprenorphine induction. Maintenance treatment continues to be superior to detoxification without continued management. Shorter induction protocols allow patients to initiate evidence-based treatment more quickly, reducing the use of illicit or non-prescribed substances.