Role of C-Reactive Protein as a Predictor of Difficult Laparoscopic Cholecystectomy

INTRODUCTION

Cholelithiasis is one of the most common diseases encountered in gastroenterology. Laparoscopic cholecystectomy can be labelled as difficult if the surgery continues for more than 60 minutes or if the cystic artery is injured before ligation or clipping. Predicting difficult laparoscopic cholecystectomy can help the surgeon to be prepared for intraoperative challenges such as adhesions in triangle of Calot, injury to cystic artery or gall stone spillage; and improve patient counseling.

METHODS

In this cross-sectional study, we evaluated 269 patients with diagnosed cholelithiasis and planned for laparoscopic cholecystectomy in the general surgery department of Civil Hospital Karachi. After approval of the institution review board of the Civil Hospital, the data of all the patients was collected along with informed consent. The patients were selected via nonprobability, consecutive sampling.

RESULTS

The prevalence of difficult LC during procedure was 14.5% (39/269). Contingency table showed the true positive, negative and false positive and negative observation and using these observation to compute accuracy. Sensitivity, specificity, PPV, NPV and accuracy of serum c-reactive protein (CRP) in predicting the difficult laparoscopic cholecystectomy in patients of cholelithiasis was 87.2%, 97%, 82.9%, 97.8% and 95.5% respectively. Effect modifiers like age, gender and BMI were controlled by stratification analysis and observed that diagnostic accuracy was above 90% in all stratified groups as presented in the following tables. 175 (65.06%) of 279 patients were females indicating female predominance. In general, 41 patients (15.05%) had CRP serum levels greater than 11 mg/dL out of which 34 patients had to undergo difficult laparoscopic cholecystectomy (DLC), while 223 out of 228 patients with serum CRP levels of less than 11 mg/dL did not face any difficulty during their cholecystectomy. Similar results have been acquired across all age groups and both genders.

CONCLUSION

C Reactive Protein is a potent predictor of difficult laparoscopic cholecystectomy and its conversion preoperatively. Patients with preoperatively high C Reactive Protein CRP levels in serum have more chances of complication intraoperatively and increased chances of conversion from laparoscopic to open surgery. Preoperative C Reactive Protein (CRP) with values >11 mg/dL was associated with the highest odds of presenting difficult laparoscopic cholecystectomy (DLC) in our study. This value possesses good sensitivity, specificity, PPV, and NPV for predicting DLC in our population.

Unlocking integrated waste biorefinery approach by predicting calorific value of waste biomass

The current study analyzed the high heating values (HHVs) of various waste biomass materials intending to the effective management and more sustainable consumption of waste as clean energy source. Various biomass waste samples including date leaves, date branches, coconut leaves, grass, cooked macaroni, salad, fruit and vegetable peels, vegetable scraps, cooked food waste, paper waste, tea waste, and cardboard were characterized for proximate analysis. The results revealed that all the waste biomass were rich in organic matter (OM). The total OM for all waste biomass ranged from 79.39% to 98.17%. Likewise, the results showed that all the waste biomass resulted in lower ash content and high fixed carbon content associated with high fuel quality. Based on proximate analysis, various empirical equations (HHV=28.296-0.2887(A)-656.2/VM, HHV=18.297-0.4128(A)+35.8/FC and HHV=22.3418-0.1136(FC)-0.3983(A)) have been tested to predict HHVs. It was observed that the heterogeneous nature of various biomass waste considerably affects the HHVs and hence has different fuel characteristics. Similarly, the HHVs of waste biomass were also determined experimentally using the bomb calorimeter, and it was observed that among all the selected waste biomass, the highest HHVs (21.19 MJ kg-1) resulted in cooked food waste followed by cooked macaroni (20.25 MJ kg-1). The comparison revealed that experimental HHVs for the selected waste biomass were slightly deviated from the predicted HHVs. Based on HHVs, various thermochemical and biochemical technologies were critically overviewed to assess the suitability of waste biomass to energy products. It has been emphasized that valorizing waste-to-energy technologies provides the dual benefits of sustainable management and production of cleaner energy to reduce fossil fuels dependency. However, the key bottleneck in commercializing waste-to-energy systems requires proper waste collection, sorting, and continuous feedstock supply. Moreover, related stakeholders should be involved in designing and executing the decision-making process to facilitate the global recognition of waste biorefinery concept.

Detecting visually significant cataract using retinal photograph-based deep learning

Age-related cataracts are the leading cause of visual impairment among older adults. Many significant cases remain undiagnosed or neglected in communities, due to limited availability or accessibility to cataract screening. In the present study, we report the development and validation of a retinal photograph-based, deep-learning algorithm for automated detection of visually significant cataracts, using more than 25,000 images from population-based studies. In the internal test set, the area under the receiver operating characteristic curve (AUROC) was 96.6%. External testing performed across three studies showed AUROCs of 91.6-96.5%. In a separate test set of 186 eyes, we further compared the algorithm's performance with 4 ophthalmologists' evaluations. The algorithm performed comparably, if not being slightly more superior (sensitivity of 93.3% versus 51.7-96.6% by ophthalmologists and specificity of 99.0% versus 90.7-97.9% by ophthalmologists). Our findings show the potential of a retinal photograph-based screening tool for visually significant cataracts among older adults, providing more appropriate referrals to tertiary eye centers.

Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: a retrospective multicohort study

BACKGROUND

By 2050, almost 5 billion people globally are projected to have myopia, of whom 20% are likely to have high myopia with clinically significant risk of sight-threatening complications such as myopic macular degeneration. These are diagnoses that typically require specialist assessment or measurement with multiple unconnected pieces of equipment. Artificial intelligence (AI) approaches might be effective for risk stratification and to identify individuals at highest risk of visual loss. However, unresolved challenges for AI medical studies remain, including paucity of transparency, auditability, and traceability.

METHODS

In this retrospective multicohort study, we developed and tested retinal photograph-based deep learning algorithms for detection of myopic macular degeneration and high myopia, using a total of 226 686 retinal images. First we trained and internally validated the algorithms on datasets from Singapore, and then externally tested them on datasets from China, Taiwan, India, Russia, and the UK. We also compared the performance of the deep learning algorithms against six human experts in the grading of a randomly selected dataset of 400 images from the external datasets. As proof of concept, we used a blockchain-based AI platform to demonstrate the real-world application of secure data transfer, model transfer, and model testing across three sites in Singapore and China.

FINDINGS

The deep learning algorithms showed robust diagnostic performance with areas under the receiver operating characteristic curves [AUC] of 0·969 (95% CI 0·959-0·977) or higher for myopic macular degeneration and 0·913 (0·906-0·920) or higher for high myopia across the external testing datasets with available data. In the randomly selected dataset, the deep learning algorithms outperformed all six expert graders in detection of each condition (AUC of 0·978 [0·957-0·994] for myopic macular degeneration and 0·973 [0·941-0·995] for high myopia). We also successfully used blockchain technology for data transfer, model transfer, and model testing between sites and across two countries.

INTERPRETATION

Deep learning algorithms can be effective tools for risk stratification and screening of myopic macular degeneration and high myopia among the large global population with myopia. The blockchain platform developed here could potentially serve as a trusted platform for performance testing of future AI models in medicine.

FUNDING

None.

Congenital dengue in a Saudi neonate: A case report

BACKGROUND

Dengue fever is a mosquito-borne viral illness with 100 million new cases occurring worldwide. The vectors involved are mainly Aedes aegypti and Aedes albopictus. Dengue infection is associated with maternal as well as fetal morbidities, like stillbirth, preterm birth, and low birth weight.

THE CASE

We report a case of dengue fever occurring during early pregnancy and subsequent congenital neurologic malformation in the neonate as a result of vertical transmission. To our knowledge, this is the first case of confirmed congenital dengue in Saudi Arabia.

DISCUSSION

Dengue infection is not commonly associated with congenital anomalies and no biologic mechanism has yet been established for its teratogenicity. Congenital dengue in neonates can be confirmed by identification of the dengue virus in cord blood samples. The positive dengue serology within the first week of life, together with the confirmed maternal dengue infection during pregnancy, supports the diagnosis of vertical transmission in the presence of clinical manifestations. A high index of suspicion, early diagnosis, and close monitoring is needed in these cases.

High-content image generation for drug discovery using generative adversarial networks

Immense amount of high-content image data generated in drug discovery screening requires computationally driven automated analysis. Emergence of advanced machine learning algorithms, like deep learning models, has transformed the interpretation and analysis of imaging data. However, deep learning methods generally require large number of high-quality data samples, which could be limited during preclinical investigations. To address this issue, we propose a generative modeling based computational framework to synthesize images, which can be used for phenotypic profiling of perturbations induced by drug compounds. We investigated the use of three variants of Generative Adversarial Network (GAN) in our framework, viz., a basic Vanilla GAN, Deep Convolutional GAN (DCGAN) and Progressive GAN (ProGAN), and found DCGAN to be most efficient in generating realistic synthetic images. A pre-trained convolutional neural network (CNN) was used to extract features of both real and synthetic images, followed by a classification model trained on real and synthetic images. The quality of synthesized images was evaluated by comparing their feature distributions with that of real images. The DCGAN-based framework was applied to high-content image data from a drug screen to synthesize high-quality cellular images, which were used to augment the real image data. The augmented dataset was shown to yield better classification performance compared with that obtained using only real images. We also demonstrated the application of proposed method on the generation of bacterial images and computed feature distributions for bacterial images specific to different drug treatments. In summary, our results showed that the proposed DCGAN-based framework can be utilized to generate realistic synthetic high-content images, thus enabling the study of drug-induced effects on cells and bacteria.

Artificial Intelligence for Cataract Detection and Management

The rising popularity of artificial intelligence (AI) in ophthalmology is fuelled by the ever-increasing clinical "big data" that can be used for algorithm development. Cataract is one of the leading causes of visual impairment worldwide. However, compared with other major age-related eye diseases, such as diabetic retinopathy, age-related macular degeneration, and glaucoma, AI development in the domain of cataract is still relatively underexplored. In this regard, several previous studies explored algorithms for automated cataract assessment using either slit lamp of color fundus photographs. However, several other study groups proposed or derived new AI-based calculation for pre-cataract surgery intraocular lens power. Along with advancements in digitization of clinical data, data curation for future cataract-related AI developmental work is bound to undergo significant improvements in the foreseeable future. Even though most of these previous studies reported early promising performances, limitations such as lack of robust, high-quality training data, and lack of external validations remain. In the next phase of work, apart from algorithm's performance, it will also be pertinent to evaluate deployment angles, feasibility, efficiency, and cost-effectiveness of these new cataract-related AI systems.

Isolated Dorsal Pancreatic Agenesis

Developmental anomalies of the pancreas have been reported; but among these, agenesis of dorsal pancreas is an extremely rare congenital pancreatic anomaly. It may be asymptomatic and incidentally detected on imaging or may be associated with diabetes mellitus or attacks of pancreatitis. We report a rare case of agenesis of dorsal pancreas that was detected incidentally on imaging and there was no other co-existing anomaly.

Evaluation of the effect of temperature, NaOH concentration and time on solubilization of palm oil mill effluent (POME) using response surface methodology (RSM)

In this study, palm oil mill effluent (POME) was solubilized by batch thermo-alkaline pre-treatments. A three-factor central composite design (CCD) was applied to identify the optimum COD solubilization condition. The individual and interactive effects of three factors, temperature, NaOH concentration and reaction time, on solubilization of POME were evaluated by employing response surface methodology (RSM). The experimental results showed that temperature, NaOH concentration and reaction time all had an individual significant effect on the solubilization of POME. But these three factors were independent, or there was insignificant interaction on the response. The maximum COD solubilization of 82.63% was estimated under the optimum condition at 32.5 degrees C, 8.83g/L of NaOH and 41.23h reaction time. The confirmation experiment of the predicted optimum conditions verified that the RSM with the central composite design was useful for optimizing the solubilization of POME.

Abnormal bone and gallium scans in a case of multifocal systemic sporotrichosis

Sporotrichosis is a chronic infection of world wide distribution. It is caused by the diamorphic fungus Sporotrichum schenkii. The multifocal form of sporotrichosis usually involves skin, joints, long bones, lungs, and lymph nodes. Involvement of central nervous system although described is extremely rare. Bone and gallium imaging findings in a case of multifocal systemic sporotrichosis which nicely demonstrated the extensive spread of the disease are presented.