Traveling together! - Intrathymic thyroid tissue in a patient with Graves' disease.

Ectopic thyroid tissue is rare in the general population and more prevalent in people who have existing thyroid disease. Common anatomical sites of ectopic thyroid tissue include the lateral cervical region, thyroglossal duct, mediastinum, lingual, sublingual, and submandibular region. Intrathymic ectopic thyroid tissue is exceedingly rare. The purpose of this report is to describe one such case in a 52-year-old African-American female with Graves' disease. The patient presented for a physical exam and follow-up. During the exam, an incidental mediastinal mass was discovered, which was evaluated by imaging studies and subsequently was resected. Histologically, the mass was composed of variable-sized thyroid follicles lined by a monolayer of cuboidal to columnar follicular epithelial cells and filled with eosinophilic colloid, surrounded by a rim of unremarkable compressed thymic tissue.

Neuro-XAI: Explainable deep learning framework based on deeplabV3+ and bayesian optimization for segmentation and classification of brain tumor in MRI scans.

The prevalence of brain tumor disorders is currently a global issue. In general, radiography, which includes a large number of images, is an efficient method for diagnosing these life-threatening disorders. The biggest issue in this area is that it takes a radiologist a long time and is physically strenuous to look at all the images. As a result, research into developing systems based on machine learning to assist radiologists in diagnosis continues to rise daily. Convolutional neural networks (CNNs), one type of deep learning approach, have been pivotal in achieving state-of-the-art results in several medical imaging applications, including the identification of brain tumors. CNN hyperparameters are typically set manually for segmentation and classification, which might take a while and increase the chance of using suboptimal hyperparameters for both tasks. Bayesian optimization is a useful method for updating the deep CNN's optimal hyperparameters. The CNN network, however, can be considered a "black box" model because of how difficult it is to comprehend the information it stores because of its complexity. Therefore, this problem can be solved by using Explainable Artificial Intelligence (XAI) tools, which provide doctors with a realistic explanation of CNN's assessments. Implementation of deep learning-based systems in real-time diagnosis is still rare. One of the causes could be that these methods don't quantify the Uncertainty in the predictions, which could undermine trust in the AI-based diagnosis of diseases. To be used in real-time medical diagnosis, CNN-based models must be realistic and appealing, and uncertainty needs to be evaluated. So, a novel three-phase strategy is proposed for segmenting and classifying brain tumors. Segmentation of brain tumors using the DeeplabV3+ model is first performed with tuning of hyperparameters using Bayesian optimization. For classification, features from state-of-the-art deep learning models Darknet53 and mobilenetv2 are extracted and fed to SVM for classification, and hyperparameters of SVM are also optimized using a Bayesian approach. The second step is to understand whatever portion of the images CNN uses for feature extraction using XAI algorithms. Using confusion entropy, the Uncertainty of the Bayesian optimized classifier is finally quantified. Based on a Bayesian-optimized deep learning framework, the experimental findings demonstrate that the proposed method outperforms earlier techniques, achieving a 97 % classification accuracy and a 0.98 global accuracy.

Efficacy of oral anticoagulants in chronic kidney disease and hemodialysis patients with atrial fibrillation: a systematic review and meta-analysis.

INTRODUCTION: This meta-analysis seeks to evaluate the efficacy of direct oral anticoagulants (DOACs) versus vitamin K antagonists (VKA) in individuals with chronic kidney disease (CKD), end-stage renal disease (ESRD), and undergoing hemodialysis (HD) who also have atrial fibrillation (AF). EVIDENCE ACQUISITION: A comprehensive search of MEDLINE, clinicaltrials.gov, EMBASE, and Cochrane Database for relevant studies reporting the usefulness of OAC therapy for CKD, ESRD, and HD patients with AF was conducted from its inception until 1st May 2023. The studies that reported OR, RR, or HR for adult AF patients to investigate the efficacy of OAC in CKD, ESRD, and HD were included. Statistical analysis was completed using a generic inverse variance and random-effects model to calculate the combined HR and their corresponding 95% CIs for all outcomes. EVIDENCE SYNTHESIS: The meta-analysis included 33 studies with 178,956 patients. The analysis revealed that the DOACs, when compared to VKA, significantly lowered the risk of stroke or systemic embolism (HR: 0.81 [95% CI: 0.70, 0.93]; P=0.002; I2=62%), bleeding (HR: 0.77, [95% CI: 0.67, 0.89]; P=0.0003; I2=83%), and intracranial hemorrhage (HR: 0.56, [95% CI 0.47, 0.66]; P<0.00001; I2=0%). Similarly, the risks of cardiovascular death (HR: 0.88, [95% CI 0.78, 1.00]; P=0.05; I2=0%), all-cause mortality (HR: 0.88, [95% CI 0.70, 1.10]; P=0.25; I2=96%), and myocardial infarction (HR: 0.80, [95% CI 0.54, 1.17]; P= 0.25; I2= 0%) were lowered by DOAC, but the result was insignificant. No significant difference was seen in the risk of gastrointestinal bleeding between DOAC and VKA as well (HR: 0.95, [95% CI 0.75, 1.20]; P=0.65; I2=83%). CONCLUSIONS: Our meta-analysis confirms that DOACs are effective for managing AF in patients with kidney disease, with potential clinical implications for AF and CKD management. Further research should explore DOACs' reno-protective effects.

ET-Network: A novel efficient transformer deep learning model for automated Urdu handwritten text recognition.

Automatic Urdu handwritten text recognition is a challenging task in the OCR industry. Unlike printed text, Urdu handwriting lacks a uniform font and structure. This lack of uniformity causes data inconsistencies and recognition issues. Different writing styles, cursive scripts, and limited data make Urdu text recognition a complicated task. Major languages, such as English, have experienced advances in automated recognition, whereas low-resource languages, such as Urdu, still lag. Transformer-based models are promising for automated recognition in high- and low-resource languages such as Urdu. This paper presents a transformer-based method called ET-Network that integrates self-attention into EfficientNet for feature extraction and a transformer for language modeling. The use of self-attention layers in EfficientNet helps to extract global and local features that capture long-range dependencies. These features proceeded into a vanilla transformer to generate text, and a prefix beam search is used for the finest outcome. NUST-UHWR, UPTI2.0, and MMU-OCR-21 are three datasets used to train and test the ET Network for a handwritten Urdu script. The ET-Network improved the character error rate by 4% and the word error rate by 1.55%, while establishing a new state-of-the-art character error rate of 5.27% and a word error rate of 19.09% for Urdu handwritten text.

Prediction of Alzheimer's disease stages based on ResNet-Self-attention architecture with Bayesian optimization and best features selection.

Alzheimer's disease (AD) is a neurodegenerative illness that impairs cognition, function, and behavior by causing irreversible damage to multiple brain areas, including the hippocampus. The suffering of the patients and their family members will be lessened with an early diagnosis of AD. The automatic diagnosis technique is widely required due to the shortage of medical experts and eases the burden of medical staff. The automatic artificial intelligence (AI)-based computerized method can help experts achieve better diagnosis accuracy and precision rates. This study proposes a new automated framework for AD stage prediction based on the ResNet-Self architecture and Fuzzy Entropy-controlled Path-Finding Algorithm (FEcPFA). A data augmentation technique has been utilized to resolve the dataset imbalance issue. In the next step, we proposed a new deep-learning model based on the self-attention module. A ResNet-50 architecture is modified and connected with a self-attention block for important information extraction. The hyperparameters were optimized using Bayesian optimization (BO) and then utilized to train the model, which was subsequently employed for feature extraction. The self-attention extracted features were optimized using the proposed FEcPFA. The best features were selected using FEcPFA and passed to the machine learning classifiers for the final classification. The experimental process utilized a publicly available MRI dataset and achieved an improved accuracy of 99.9%. The results were compared with state-of-the-art (SOTA) techniques, demonstrating the improvement of the proposed framework in terms of accuracy and time efficiency.

Rapid Development of Achalasia After SARS-CoV-2 Infection: Polymerase Chain Reaction Analysis of Esophageal Muscle Tissue.

INTRODUCTION: Achalasia has been linked to viruses. We have observed cases of rapid-developing achalasia post-coronavirus disease 2019 (COVID-19). METHODS: We aimed to prospectively evaluate esophageal muscle for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) from patients with rapid-onset achalasia post-COVID-19 and compare them with achalasia predating COVID-19 and achalasia with no COVID-19. RESULTS: Compared with long-standing achalasia predating COVID-19 and long-standing achalasia with no COVID-19, the subjects with achalasia post-COVID-19 had significantly higher levels of messenger RNA for the SARS-CoV-2 nucleocapsid (N) protein, which correlated with a significant increase in the inflammatory markers NOD-like receptor family pyrin domain-containing 3 and tumor necrosis factor. DISCUSSION: SARS-CoV-2, the virus responsible for COVID-19, is a possible trigger for achalasia.

Traveling together! - Intrathymic thyroid tissue in a patient with Graves' disease

ABSTRACT Ectopic thyroid tissue is rare in the general population and more prevalent in people who have existing thyroid disease. Common anatomical sites of ectopic thyroid tissue include the lateral cervical region, thyroglossal duct, mediastinum, lingual, sublingual, and submandibular region. Intrathymic ectopic thyroid tissue is exceedingly rare. The purpose of this report is to describe one such case in a 52-year-old African-American female with Graves' disease. The patient presented for a physical exam and follow-up. During the exam, an incidental mediastinal mass was discovered, which was evaluated by imaging studies and subsequently was resected. Histologically, the mass was composed of variable-sized thyroid follicles lined by a monolayer of cuboidal to columnar follicular epithelial cells and filled with eosinophilic colloid, surrounded by a rim of unremarkable compressed thymic tissue.

Maximizing biodiesel yield of a non-edible chinaberry seed oil via microwave assisted transesterification process using response surface methodology and artificial neural network techniques.

In this study, the non-edible Chinaberry Seed Oil (CBO) is converted into biodiesel using microwave assisted transesterification. The objective of this effort is to maximize the biodiesel yield by optimizing the operating parameters, such as catalyst concentration, methanol-oil ratio, reaction speed, and reaction time. The designed setup provides a controlled and effective approach for turning CBO into biodiesel, resulting in encouraging yields and reduced reaction times. The experimental findings reveal the optimal parameters for the highest biodiesel yield (95 %) are a catalyst concentration of 1.5 w/w, a methanol-oil ratio of 6:1 v/v, a reaction speed of 400 RPM, and a reaction period of 3 min. The interaction of the several operating parameters on biodiesel yield has been investigated using two methodologies: Response Surface Methodology (RSM) and Artificial Neural Network (ANN). RSM provides better modeling of parameter interaction, while ANN exhibits lower comparative error when predicting biodiesel yield based on the reaction parameters. The percentage improvement in prediction of biodiesel yield by ANN is found to be 12 % as compared to RSM. This study emphasizes the merits of both the approaches for biodiesel yield optimization. Furthermore, the scaling up this microwave-assisted transesterification system for industrial biodiesel production has been proposes with focus on its economic viability and environmental effects.

Reduction in Arterial Stiffness Index (SI) in Response to Combination Antioxidant Therapy.

Antioxidants reduce arterial stiffness, but the effects previously reported are weak. A systematic review of the antioxidants vitamin E, vitamin C, vitamin A, and beta-carotenes (the most commonly studied antioxidants) on pulse wave velocity (PWV) found an effect size of only -0.20 (approximately -16 m/s or -2.5%). Studies in rats of the potent pro-oxidant substance acetaldehyde have shown that combinations of sulfur-containing antioxidants, including thiamine and l-cysteine, with ascorbic acid potently protect against oxidative-stress-mediated mortality. The effects of these combinations of oxidants on PWV have not been studied. The present study evaluated the effects of 2 weeks of therapy with a combination of sulfur-containing antioxidants (cysteine, thiamine, and pyridoxine) in combination with ascorbic acid on stiffness index (SI), a measure of arterial stiffness that is strongly correlated with PWV, using a Pulse Trace recorder in a diverse group of 78 volunteers. SI fell by -1.7 m/s relative to placebo (95% confidence intervals -0.6 to -2.7 m/s), a reduction of -19% (95% confidence intervals -9% to -31%). The Glass effect size was 1.4, indicating a very strong treatment effect which was substantially greater than the effect size found in previous studies of antioxidants. PWV reduction was correlated significantly with increasing age. Further studies of similar antioxidant combinations are required to determine whether they are of value in the treatment or prevention of cardiovascular disease.

Persistent Apoptotic Enteropathy in an Individual With Marginal Cell Lymphoma.

Apoptotic enteropathy is a histological finding of increased crypt apoptosis that is commonly associated with diarrhea. Several etiologies for this disease state including immunodeficiency, autoimmune, infection, hereditary, checkpoint inhibitors, immunosuppressants, and immunomodulators have been previously described. We describe an extremely rare case of a patient with marginal cell lymphoma treated with bendamustine, rituximab, and obinutuzumab presenting with severe, relapsing, chronic diarrhea with persistent apoptotic enteropathy of unknown etiology, despite hematological remission and discontinuation of treatment for 1 year.

Cerebral Venous Thrombosis in Young Adults: Unknown Origins.

Cerebral venous thrombosis (CVT) is a relatively rare neurological disorder that may result in significant morbidity if not diagnosed and managed promptly. The clinical presentation of CVT is nonspecific and highly variable with acute, subacute, or chronic onset. It most often presents as a headache but may present with focal neurological symptoms, symptoms of intracranial hypertension, or encephalopathy. The predisposing factors for CVT are mainly acquired and genetic hypercoagulable conditions. However, the epidemiology, predisposing factors, and clinical presentation of CVT are not clearly established given the rare nature of the condition. We present a case series of three young patients who did not have any classic underlying etiology for CVT or any prior diagnosis of venous thrombosis. We want to report this case series to show that a high index of suspicion should be maintained regardless of the absence of risk factors.

Cerebral Venous Thrombosis in a Patient With Smith-Magenis Syndrome.

Smith-Magenis syndrome (SMS) is a complex genetic developmental disorder characterized by distinctive physical features, cognitive impairment, developmental delay, and behavioral abnormalities. It is caused by a microdeletion of chromosome band 17p11.2 encoding for multiple genes including the Retinoic-acid-induced (RAI1) gene. RAI1 gene is expressed in many tissues, acting as a transcriptional regulator. It is a dosage-sensitive gene. The variants of the RAI1 gene have been explored with some contributing to systemic manifestations. The hematological manifestations such as venous thrombosis (VT) including cerebral venous thrombosis (CVT) have not been reported to date. We report a case of a 25-year-old female with SMS who presented with lethargy and gastrointestinal symptoms and was diagnosed with CVT. Our case highlights the risk of VT in patients with SMS and therefore holding a high index of suspicion for early diagnosis and management.

Anticancer Activity of Novel Difluorinated Curcumin Analog and Its Inclusion Complex with 2-Hydroxypropyl-ß-Cyclodextrin against Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) is the primary reason for cancer-related deaths in the US. Genetic mutations, drug resistance, the involvement of multiple signaling pathways, cancer stem cells (CSCs), and desmoplastic stroma, which hinders drug penetrance, contribute to poor chemotherapeutic efficacy. Hence, there is a need to identify novel drugs with improved delivery to improve treatment outcomes. Curcumin is one such compound that can inhibit multiple signaling pathways and CSCs. However, curcumin's clinical applicability for treating PDAC is limited because of its poor solubility in water and metabolic instability. Hence, we developed a difluorinated curcumin (CDF) analog that accumulates selectively in the pancreas and inhibits PDAC growth in vitro and in vivo. In the present work, we developed its 2-hydroxy-propyl-ß-cyclodextrin (HCD) inclusion complex to increase its water solubility and hydrolytic stability. The CDFHCD inclusion complex was characterized by spectroscopic, thermal, and microscopic techniques. The inclusion complex exhibited increased aqueous solubility, hydrolytic stability, and antiproliferative activity compared to parent CDF. Moreover, CDF and CDFHCD inhibited colony and spheroid formation, and induced cell cycle and apoptosis in PDAC cell lines. Hence, CDFHCD self-assembly is an efficient approach to increase water solubility and anticancer therapeutic efficacy, which now warrants advancement towards a clinical proof of concept in PDAC patients.

Spectrum of Histologic Findings in Orchiectomy Specimens of Patients Seeking Male to Female Physical Adaptation.

Context. Hormonal therapy followed by orchiectomy is of the standard of care in management of gender identity disorder in patients seeking male to female transition. The orchiectomy specimens from these patients are routinely subjected to histopathologic evaluation. We discuss the spectrum of histopathologic findings, incidental findings, and cost analysis of processing these specimens. Design. Orchiectomy specimens from patients seeking male to female transition received at our institution from January 2019 to June 2021 were included in the study. Data including patient age, history of hormonal therapy, testicular weight, histopathologic findings, number of tissue sections, and processing cost were collected. Results. A total of 79 specimens were identified. Mean patient age was 36.7 ± 14.5 years. Mean testicular weight was 28.0 ± 8.3 g (right) and 27.8 ± 9.1 g (left). Histologic evaluation showed diminished or absent spermatogenesis in 100% and fibrosis of seminiferous tubules in 96% of specimens. Benign, incidental findings, none of which altered patient management were present in 6 specimens (8%). For most specimens, 3 sections per testis were submitted. This resulted in a mean of 5.8 ± 1.1 tissue sections submitted per specimen. Conclusions. Orchiectomy specimens from patients with gender dysphoria always demonstrate hormone-therapy effects albeit with varying degree. The chances of discovering any incidental finding of clinical significance are negligible. Diligent gross inspection and minimal tissue sampling with additional sampling reserved for gross abnormalities can adequately document the histologic findings in a cost-effective manner.

Integrative Clinical and Genomic Characterization of MTAP-deficient Metastatic Urothelial Cancer.

Deficiency of MTAP (MTAPdef) mainly occurs because of homozygous loss of chromosome 9p21, which is the most common copy-number loss in metastatic urothelial cancer (mUC). We characterized the clinical and genomic features of MTAPdef mUC in 193 patients treated at MD Anderson Cancer Center (MDACC) and 298 patients from the phase 2 IMvigor210 trial, which investigated atezolizumab in cisplatin-ineligible and platinum-refractory disease. In the MDACC cohort, visceral metastases were significantly more common for MTAPdef (n = 48) than for MTAP-proficient (MTAPprof; n = 145) patients (75% vs 55.2%; p = 0.02). MTAPdef was associated with poor prognosis (median overall survival [mOS] 12.3 vs 20.2 mo; p = 0.007) with an adjusted hazard ratio of 1.93 (95% confidence interval 1.35-2.98). Similarly, IMvigor210 patients with MTAPlo (n = 29) had a higher incidence of visceral metastases than those with MTAPhi tumors (n = 269; 86.2% vs 72.5%; p = 0.021) and worse prognosis (mOS 8.0 vs 11.3 mo; p = 0.042). Hyperplasia-associated genes were more frequently mutated in MTAPdef tumors (FGFR3: 31% vs 8%; PI3KCA: 31% vs 19%), while alterations in dysplasia-associated genes were less common in MTAPdef tumors (TP53: 41% vs 67%; RB1: 0% vs 16%). Our findings support a distinct biology in MTAPdef mUC that is associated with early visceral disease and worse prognosis. PATIENT SUMMARY: We investigated the outcomes for patients with the most common gene loss (MTAP gene) in metastatic cancer of the urinary tract. We found that this loss correlates with worse prognosis and a higher risk of metastasis in internal organs. There seems to be distinct tumor biology for urinary tract cancer with MTAP gene loss and this could be a potential target for treatment.

Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound planes.

Despite a worldwide decline in maternal mortality over the past two decades, a significant gap persists between low- and high-income countries, with 94% of maternal mortality concentrated in low and middle-income nations. Ultrasound serves as a prevalent diagnostic tool in prenatal care for monitoring fetal growth and development. Nevertheless, acquiring standard fetal ultrasound planes with accurate anatomical structures proves challenging and time-intensive, even for skilled sonographers. Therefore, for determining common maternal fetuses from ultrasound images, an automated computer-aided diagnostic (CAD) system is required. A new residual bottleneck mechanism-based deep learning architecture has been proposed that includes 82 layers deep. The proposed architecture has added three residual blocks, each including two highway paths and one skip connection. In addition, a convolutional layer has been added of size 3 × 3 before each residual block. In the training process, several hyper parameters have been initialized using Bayesian optimization (BO) rather than manual initialization. Deep features are extracted from the average pooling layer and performed the classification. In the classification process, an increase occurred in the computational time; therefore, we proposed an improved search-based moth flame optimization algorithm for optimal feature selection. The data is then classified using neural network classifiers based on the selected features. The experimental phase involved the analysis of ultrasound images, specifically focusing on fetal brain and common maternal fetal images. The proposed method achieved 78.5% and 79.4% accuracy for brain fetal planes and common maternal fetal planes. Comparison with several pre-trained neural nets and state-of-the-art (SOTA) optimization algorithms shows improved accuracy.

COVID-19 classification using chest X-ray images based on fusion-assisted deep Bayesian optimization and Grad-CAM visualization.

The COVID-19 virus's rapid global spread has caused millions of illnesses and deaths. As a result, it has disastrous consequences for people's lives, public health, and the global economy. Clinical studies have revealed a link between the severity of COVID-19 cases and the amount of virus present in infected people's lungs. Imaging techniques such as computed tomography (CT) and chest x-rays can detect COVID-19 (CXR). Manual inspection of these images is a difficult process, so computerized techniques are widely used. Deep convolutional neural networks (DCNNs) are a type of machine learning that is frequently used in computer vision applications, particularly in medical imaging, to detect and classify infected regions. These techniques can assist medical personnel in the detection of patients with COVID-19. In this article, a Bayesian optimized DCNN and explainable AI-based framework is proposed for the classification of COVID-19 from the chest X-ray images. The proposed method starts with a multi-filter contrast enhancement technique that increases the visibility of the infected part. Two pre-trained deep models, namely, EfficientNet-B0 and MobileNet-V2, are fine-tuned according to the target classes and then trained by employing Bayesian optimization (BO). Through BO, hyperparameters have been selected instead of static initialization. Features are extracted from the trained model and fused using a slicing-based serial fusion approach. The fused features are classified using machine learning classifiers for the final classification. Moreover, visualization is performed using a Grad-CAM that highlights the infected part in the image. Three publically available COVID-19 datasets are used for the experimental process to obtain improved accuracies of 98.8, 97.9, and 99.4%, respectively.

Chronic Myeloid Leukaemia In A 14-Year-Old Child: An Unusual Case Report.

Chronic myeloid leukaemia is a myeloproliferative stem cell disorder characterized by dysregulated production and proliferation of myeloid cells. Hallmark of the disease is the reciprocal translocation between chromosome 9 and 22 giving rise to an abnormal chromosome known as Philadelphia chromosome. Approximately 15% of all leukaemias are chronic myeloid leukaemia with a slight male predominance. This is a case of a 14-year-old boy with no premorbid presented with 4 months history of fatigue and shortness of breath on exertion. On examination patient was vitally stable. On general physical examination there was pallor and sternal tenderness. On abdominal examination spleen was palpable with a size of about 8cm. Respiratory, cardiovascular and musculoskeletal examination was unremarkable. Complete blood picture showed leukocytosis, low haemoglobin and normal platelets. Erythrocyte sedimentation rate was 65 mm/hr. Liver function tests, Renal function tests, Serum electrolytes, Urine routine examination and c-reactive protein were normal. Chest x-ray and Electrocardiography was normal. Peripheral blood smear showed neutrophils 56% with 3% lymphocytes, 1% blasts and retic count of 0.5%. Bone marrow biopsy was suggestive of chronic myeloid leukaemia which was confirmed by FISH and Cytogenetic studies.

Is a Preservation Solution for Living Donor Liver Transplantation Needed? Adding a New Chapter in LDLT!

Preservation solutions are required for organ viability in deceased donor liver transplantation (LT). However, their role in live donor LT (LDLT) has not been standardized. Methods: Eighty adult recipients who underwent right lobe LDLT at the Department of Liver Transplantation Surgery, Gambat, Pakistan, were studied. Based on shorter cold ischemia time and no back table reconstruction work, recipients were assigned to receive "no preservation solution" (cases/non-histidine-tryptophan-ketoglutarate group; n = 40) or "HTK group" (controls; n = 40). Early allograft dysfunction (bilirubin, transaminases, and international normalized ratio), postoperative complications (biliary and vascular), hospital stay, and 1-y survival were reported. The direct cost was also reported. Results: Demographics and clinical characteristics were comparable in the 2 groups. Comparing cases versus controls, mean bilirubin, alanine aminotransferase, aspartate aminotransferase, and international normalized ratio on postoperative day 7 were similar in the 2 groups. Five (12.5%) cases and 4 (10%) controls developed early allograft dysfunction (P = 0.72). Post-LT complications (biliary leak 2.5% in cases versus 0 in control), strictures (15% in cases versus 17.5% in controls), hepatic artery thrombosis (2.5% versus 00%)' and portal vein thrombosis (0 versus 2.5%) were comparable. Mean hospital stay (10.80 + 2.36 and 11.78 + 2.91 d) and 30 d mortality (2.5% versus 5%) were also comparable. Finally, 1-y survival based on Kaplan-Meier analysis was comparable in both groups (ie, 92.5%; non-HTK group versus 90%; HTK group) (P = 0.71). The direct cost of using a non-HTK-based approach was less than the HTK solution. Conclusion: In a selected cohort of right lobe LDLT recipients, preservation solutions can be avoided safely with comparable outcomes.