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Over the last decade Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) has been developed into a potent molecular biology tool used to rapidly modify genes or their expression in a multitude of ways. In parallel, CRISPR-based screening approaches have been developed as powerful discovery platforms for dissecting the genetic basis of cellular behavior, as well as for drug target discovery. CRISPR screens can be designed in numerous ways. Here, we give a brief background to CRISPR screens and discuss the pros and cons of different design approaches, including unbiased genome-wide screens that target all known genes, as well as hypothesis-driven custom screens in which selected subsets of genes are targeted (Fig. 1). We provide several suggestions for how a custom screen can be designed, which could broadly serve as inspiration for any experiment that includes candidate gene selection. Finally, we discuss how results from CRISPR screens could be translated into drug development, as well as future trends we foresee in the rapidly evolving CRISPR screen field.Dendritic cell (DC)-based vaccines have been largely used in the adjuvant setting for the treatment of cancer, however, despite their proven safety, clinical outcomes still remain modest. In order to improve their efficacy, DC-based vaccines are often combined with one or multiple immunomodulatory agents. However, the selection of the most promising combinations is hampered by the plethora of agents available and the unknown interplay between these different agents. To address this point, we developed a hybrid experimental and computational platform to predict the effects and immunogenicity of dual combinations of stimuli once combined with DC vaccination, based on the experimental data of a variety of assays to monitor different aspects of the immune response after a single stimulus. To assess the stimuli behavior when used as single agents, we first developed an in vitro co-culture system of T cell priming using monocyte-derived DCs loaded with whole tumor lysate to prime autologous peripheral blood mononuclear cells in the presence of the chosen stimuli, as single adjuvants, and characterized the elicited response assessing 18 different phenotypic and functional traits important for an efficient anti-cancer response. We then developed and applied a prediction algorithm, generating a ranking for all possible dual combinations of the different single stimuli considered here. The ranking generated by the prediction tool was then validated with experimental data showing a strong correlation with the predicted scores, confirming that the top ranked conditions globally significantly outperformed the worst conditions. Thus, the method developed here constitutes an innovative tool for the selection of the best immunomodulatory agents to implement in future DC-based vaccines.Fluorescence polarization microscopy (FPM) analyzes both intensity and orientation of fluorescence dipole, and reflects the structural specificity of target molecules. It has become an important tool for studying protein organization, orientational order, and structural changes in cells. However, suffering from optical diffraction limit, conventional FPM has low orientation resolution and observation accuracy, as the polarization information is averaged by multiple fluorescent molecules within a diffraction-limited volume. Recently, novel super-resolution FPMs have been developed to break the diffraction barrier. In this review, we will introduce the recent progress to achieve sub-diffraction determination of dipole orientation. Biological applications, based on polarization analysis of fluorescence dipole, are also summarized, with focus on chromophore-target molecule interaction and molecular organization.Classification of breast cancer subtypes using multi-omics profiles is a difficult problem since the data sets are high-dimensional and highly correlated. Deep neural network (DNN) learning has demonstrated advantages over traditional methods as it does not require any hand-crafted features, but rather automatically extract features from raw data and efficiently analyze high-dimensional and correlated data. We aim to develop an integrative deep learning framework for classifying molecular subtypes of breast cancer. We collect copy number alteration and gene expression data measured on the same breast cancer patients from the Molecular Taxonomy of Breast Cancer International Consortium. We propose a deep learning model to integrate the omics datasets for predicting their molecular subtypes. The performance of our proposed DNN model is compared with some baseline models. Furthermore, we evaluate the misclassification of the subtypes using the learned deep features and explore their usefulness for clustering the breast cancer patients. We demonstrate that our proposed integrative deep learning model is superior to other deep learning and non-deep learning based models. Particularly, we get the best prediction result among the deep learning-based integration models when we integrate the two data sources using the concatenation layer in the models without sharing the weights. Using the learned deep features, we identify 6 breast cancer subgroups and show that Her2-enriched samples can be classified into more than one tumor subtype. Overall, the integrated model show better performance than those trained on individual data sources.There has been increasing interest in the role of T cells and their involvement in cancer, autoimmune and infectious diseases. However, the nature of T cell receptor (TCR) epitope recognition at a repertoire level is not yet fully understood. Due to technological advances a plethora of TCR sequences from a variety of disease and treatment settings has become readily available. Current efforts in TCR specificity analysis focus on identifying characteristics in immune repertoires which can explain or predict disease outcome or progression, or can be used to monitor the efficacy of disease therapy. In this context, clustering of TCRs by sequence to reflect biological similarity, and especially to reflect antigen specificity have become of paramount importance. We review the main TCR sequence clustering methods and the different similarity measures they use, and discuss their performance and possible improvement. We aim to provide guidance for non-specialists who wish to use TCR repertoire sequencing for disease tracking, patient stratification or therapy prediction, and to provide a starting point for those aiming to develop novel techniques for TCR annotation through clustering.

Complex Regional Pain Syndrome type 1 (CRPS1) is a potential complication, affecting the prognosis of functional joint recovery. Its incidence ranges from 2 to 40% depending on the series and the joints involved. Very few studies have evaluated the incidence of CRPS after shoulder surgery. The objective of our study was to determine the incidence of CRPS1 and to identify any pre-operative risk factors associated with its emergence after extra-articular subacromial space surgery.

This is a retrospective single-centre study of patients who underwent surgery for a subacromial extra-articular shoulder pathology from January 2016 to December 2016 and included a follow-up period of at least 6months. The primary inclusion criterion was developing a CRPS1 as defined by Veldman. A pre- and post-operative clinical assessment was performed based on the Constant (Cst) score.

Among the 287 patients, with an average follow-up period of 6.5months, included in the study, 38 (13%) presented with post-operative CRPS1. Treated hypothyroidism (OR = 3.79; 95% CI 1.58;9.07;

 = 0.003), open surgery (OR = 2.92; 95% CI 1.35-6.32;

 = 0.007) and the level of daily physical activity from the Cst score (OR = 0.088; 95% CI 0.79;0.97;

 = 0.015) were found to be significantly associated with the onset of CRPS1.

CRPS1 affected more than 10% of patients who underwent surgery for a subacromial shoulder pathology. The current study identified hypothyroidism, open surgery, and pre-operative clinical status as risk factors for the onset of this complication. These parameters should, therefore, be taken into consideration during the patient's pre-operative consultation.

CRPS1 affected more than 10% of patients who underwent surgery for a subacromial shoulder pathology. The current study identified hypothyroidism, open surgery, and pre-operative clinical status as risk factors for the onset of this complication. These parameters should, therefore, be taken into consideration during the patient's pre-operative consultation.

Hallux valgus (HV) is the most common pathologic entity affecting the great toe. The goal of corrective surgery is to restore foot mechanics and provide pain relief. The purpose of the study was to create individual angle using life-size foot models with three-dimensional (3D) printing technology to design a section on HV osteotomy.

Ten female patients with a diagnosis of HV were included. Radiologic [HV angle and intermetatarsal (IM) angle] and clinical [American Orthopaedic Foot and Ankle Score (AOFAS)] assessment was done pre- and postoperatively. All the operations were planned together with 3D life-size models generated from computed tomography (CT) scans. Benefits of using the 3D life-size models were noted. The 3D model's perception was evaluated.

The mean AOFAS score, mean HV, and IM angles had improved significantly (

 < 0.05). The visual and tactile inspection of 3D models allowed the best anatomical understanding, with faster and clearer comprehension of the surgical planning. At the firsive planning stage, for intraoperative navigation. It helps to create a patient-specific angle section on osteotomy to correct IM angle better and improve postoperative foot function. The 3D personalized model allowed for a better perception of information when compared to the corresponding 3D reconstructed image provided.

Anterior interosseous nerve (AIN) syndrome is a rare disease whose pathophysiology is controversial. Despite efforts to elucidate the pathophysiology of AIN syndrome, it has not yet been resolved. We reinterpret electrodiagnostic studies, magnetic resonance imaging (MRI), and surgical findings to clarify the pathophysiology of AIN syndrome.

In this retrospective case series, we included surgically treated 20 cases of nontraumatic AIN syndrome. Surgery was performed after a minimum of 12weeks of conservative treatment. The clinical data and operation records were extracted from the medical records for analysis. All electrodiagnostic tests were reinterpreted by physicians with an American Board Certification in electrodiagnostic medicine. Moreover, every contrast-enhanced MRI performed during the assessment was reviewed by a musculoskeletal radiologist.

Of the twenty re-analyzed cases, nine AIN syndromes (45%) showed abnormal electromyography in non-AIN innervated muscles. Sensory nerve conduction studies were normal in all cases. Five magnetic resonance images (46%) showed signal changes in non-AIN-innervated muscles. Only four cases (20%) revealed definitive compression of the AIN during surgery.

Electrodiagnostic study and MRI indicated that many patients with AIN syndrome exhibited a diffuse pathologic involvement of the motor component of the median nerve. We conclude that the main pathophysiology of AIN syndrome would be diffuse motor fascicle neuritis of the median nerve in the upper arm.

Electrodiagnostic study and MRI indicated that many patients with AIN syndrome exhibited a diffuse pathologic involvement of the motor component of the median nerve. read more We conclude that the main pathophysiology of AIN syndrome would be diffuse motor fascicle neuritis of the median nerve in the upper arm.