The Production and Characterization of Novel Heavy-Chain Antibodies Against the Tandem Repeat Region of MUC1 Mucin

Camelidae are known to produce immunoglobulins (Igs) devoid of light chains and constant heavy-chain domains (CH₁). Antigen-specific fragments of these heavy-chain IgGs (VHH) are of great interest in biotechnology applications. This paper describes the first example of successfully raised heavy-chain antibodies in Camelus dromedarius (single-humped camel) and Camelus bactrianus (two-humped camel) against a MUC1 related peptide that is found to be an important epitope expressed in cancerous tissue. Camels were immunized against a synthetic peptide corresponding to the tandem repeat region of MUC1 mucin and cancerous tissue preparation obtained from patients suffering from breast carcinoma. Three IgG subclasses with different binding properties to protein A and G were purified by affinity chromatography. Both conventional and heavy-chain IgG antibodies were produced in response to MUC1-related peptide. The elicited antibodies could react specifically with the tandem repeat region of MUC1 mucin in an enzyme linked immunosorbant assay (ELISA). Anti-peptide antibodies were purified after passing antiserum over two affinity chromatography columns. Using ELISA, immunocytochemistry and Western blotting, the interaction of purified antibodies with different antigens was evaluated. The antibodies were observed to be selectively bound to antigens namely: MUC1 peptide (tandem repeat region), human milk fat globule membrane (HMFG), deglycosylated human milk fat globule membrane (D-HMFG), homogenized cancerous breast tissue and a native MUC1 purified from ascitic fluid. K_a values of specific polyclonal anti-peptide antibodies were estimated in C. dromedarius and C. bactrianus, as 7 × 10¹⁰ M^{? 1} and 1.4 × 10¹⁰ M^{? 1} respectively.     

Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach

Purpose

Functional magnetic resonance imaging (fMRI) in resting state can be used to evaluate the functional organization of the human brain in the absence of any task or stimulus. The functional connectivity (FC) has non-stationary nature and consented to be varying over time. By considering the dynamic characteristics of the FC and using graph theoretical analysis and a machine learning approach, we aim to identify the laterality in cases of temporal lobe epilepsy (TLE).

Methods

Six global graph measures are extracted from static and dynamic functional connectivity matrices using fMRI data of 35 unilateral TLE subjects. Alterations in the time trend of the graph measures are quantified. The random forest (RF) method is used for the determination of feature importance and selection of dynamic graph features including mean, variance, skewness, kurtosis, and Shannon entropy. The selected features are used in the support vector machine (SVM) classifier to identify the left and right epileptogenic sides in patients with TLE.

Results

Our results for the performance of SVM demonstrate that the utility of dynamic features improves the classification outcome in terms of accuracy (88.5% for dynamic features compared with 82% for static features). Selecting the best dynamic features also elevates the accuracy to 91.5%.

Conclusion

Accounting for the non-stationary characteristics of functional connectivity, dynamic connectivity analysis of graph measures along with machine learning approach can identify the temporal trend of some specific network features. These network features may be used as potential imaging markers in determining the epileptogenic hemisphere in patients with TLE.

     

Distress tolerance and special alcohol metacognitions behave differently in the association of negative affect with alcohol-related patterns in men with problematic alcohol use in the abstinence phase

Negative affect may be related to alcohol-related patterns (e.g., craving and problematic alcohol use). Distress intolerance and positive and negative alcohol-related metacognitions may be underlying mechanisms in this link. This study aimed to evaluate the effect of negative affect including depressive, anxious, and stress symptoms on alcohol craving and problematic alcohol use via the paths of distress tolerance and both positive and negative alcohol-related metacognitions. Three hundred men with problematic alcohol use during the abstinence phase completed psychological and clinical measures. Results showed that craving and negative alcohol metacognitions mediated the relationship between negative affect and problematic alcohol use. Negative affect had a direct and positive effect on craving and indirect effect via distress intolerance and positive alcohol metacognitions. In turn, distress intolerance and positive alcohol metacognitions indirectly and positively affected problematic alcohol use via craving. The study indicates that distress tolerance and distinct alcohol metacognitions may be differently related to various patterns of alcohol-related problems, such that alcohol drinkers with high levels of negative affect, distress intolerance, and positive alcohol metacognitions show higher levels of craving, while high negative affect in relation to high negative alcohol metacognitions and alcohol craving is related to the perpetuation of alcohol use or problematic alcohol use.     

Cone Beam Computed Tomography Analysis of Sphenoid Sinus Pneumatization and Relationship with Neurovascular Structures

BackgroundThe sphenoid sinus is considered as the most variable pneumatized structure of the skull.PurposeThe aim of the present study was to determine the prevalence of the Onodi cell as well as to evaluate the relationship between the sphenoid sinus type of pneumatization and the presence of surrounding neurovascular protrusion using cone beam computed tomography (CBCT).MethodsThe CBCT images of 500 patients/996 sides [203 males (40.6%) and 297 females (59.4%)] were analyzed in this study. The type of sphenoid sinus pneumatization, prevalence of internal carotid artery (ICA) and optic nerve (ON) protrusion and dehiscence, and also the frequency of Onodi cell were assessed.ResultsThe percentages of the conchal, presellar, sellar, postsellar (a), and postsellar (b) types of pneumatization were 1%, 11.5%, 35.5%, 38.9%, and 13.1%, respectively. The more the sphenoid sinuses pneumatized, the greater the frequency of ON and ICA protrusion and dehiscence of their wall to the sinus. The prevalence of Onodi cell was 38.8%. A significant correlation was found between ON dehiscence and the presence of Onodi cells.ConclusionThe present study demonstrated a significant relationship between the sinus type and frequency of neurovascular protrusions. Therefore, the sphenoid sinus extent of pneumatization might be useful in predicting the risk of iatrogenic damage to the surrounding structures.     

Dynamics of host immune responses to SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the most recent global health threat, is spreading throughout the world with worrisome speed, and the current wave of coronavirus disease 2019 (COVID-19) seems to have no mercy. While this mysterious virus challenges our ability to control viral infections, our opportunities to control the COVID-19 pandemic are gradually fading. Currently, pandemic management relies on preventive interventions. Although prevention is a good strategy to mitigate SARS-CoV-2 transmission, it still cannot be considered an absolute solution to eliminate this pandemic. Currently, developing a potent immunity against this viral infection seems to be the most promising strategy to drive down this ongoing global tragedy. However, with the emergence of new challenges in the context of immune responses to COVID-19, the road to control this devastating pandemic seems bumpier; thus, it is pivotal to characterize the dynamics of host immune responses to COVID-19, in order to develop efficient prophylactic and therapeutic tools. This begs the question of whether the effector mechanisms of the immune system are indeed potent or a possible contributing factor to developing more severe and lethal forms of COVID-19. In this review, the possible role of the immunopathologic phenomena including antibody-dependent enhancement, cytokine storm, and original antigenic sin in severity and mortality of COVID-19 will be discussed.     

Fascial Repair of Laparoscopic Ports with Allis–Hemostat Technique

Port site hernias are one of the most serious complications associated with laparoscopic surgery. In this study, we present a simple and reliable method for port site closure in laparoscopic surgery. From 2005 to 2011, 500 patients who underwent laparoscopic surgery were enrolled for the study. They were evaluated considering age, sex, indication of laparoscopic surgery, and early and late complications of port site and were followed up at least for 1 year after the surgery. In our study, 180 males and 320 females with mean age of 36 years were enrolled. The most common indication for laparoscopic surgery was cholecystectomy in 320 patients (64 %). There were no early or late complications of port site after surgery. Our method is a new modification of the procedure presented by Spalding. Using Allis forceps and putting it under the fascia seems to be a more suitable technique which facilitates the laparoscopic port repair. We found it to be extremely safe, simple, and easy to teach.     

Distinct neural ensemble response statistics are associated with recognition and discrimination of natural sound textures

The perception of sound textures, a class of natural sounds defined by statistical sound structure such as fire, wind, and rain, has been proposed to arise through the integration of time-averaged summary statistics. Where and how the auditory system might encode these summary statistics to create internal representations of these stationary sounds, however, is unknown. Here, using natural textures and synthetic variants with reduced statistics, we show that summary statistics modulate the correlations between frequency organized neuron ensembles in the awake rabbit inferior colliculus (IC). These neural ensemble correlation statistics capture high-order sound structure and allow for accurate neural decoding in a single trial recognition task with evidence accumulation times approaching 1 s. In contrast, the average activity across the neural ensemble (neural spectrum) provides a fast (tens of milliseconds) and salient signal that contributes primarily to texture discrimination. Intriguingly, perceptual studies in human listeners reveal analogous trends: the sound spectrum is integrated quickly and serves as a salient discrimination cue while high-order sound statistics are integrated slowly and contribute substantially more toward recognition. The findings suggest statistical sound cues such as the sound spectrum and correlation structure are represented by distinct response statistics in auditory midbrain ensembles, and that these neural response statistics may have dissociable roles and time scales for the recognition and discrimination of natural sounds.

What makes a sound natural, and what are the neural codes that support recognition and discrimination of real-world natural sounds? Although it is known that the early auditory system decomposes sounds along fundamental acoustic dimensions such as intensity and frequency, the higher-level neural computations that mediate natural sound recognition are poorly understood. This general lack of understanding is in part attributed to the structural complexity of natural sounds, which is difficult to study with traditional auditory test stimuli, such as tones, noise, or modulated sequences. Such stimuli can reveal details of the neural representation for relatively low-level acoustic cues, yet they don’t capture the rich and diverse statistical structure of natural sounds. Thus, they cannot reveal many of the computations associated with higher-level sound properties that facilitate auditory tasks such as natural sound recognition or discrimination. A class of stationary natural sounds termed textures, such as the random sounds emanating from a running stream, a crowded restaurant, or a chorus of birds, have been proposed as alternative natural stimuli which allow for manipulating high-level acoustic structure (1). Texture sounds are composed of spatially and temporally distributed acoustic elements that are collectively perceived as a single source and are defined by their statistical features. Identification of these natural sounds has been proposed to be mediated through the integration of time-averaged summary statistics, which account for high-level structures such as the sparsity and time-frequency correlation structure found in many natural sounds (1–3). Using a generative model of the auditory system to measure summary statistics from natural texture sounds, it is possible to synthesize highly realistic synthetic auditory textures (1). This suggests that high-order statistical cues are perceptually salient and that the brain might extract these statistical features to build internal representations of sounds.Although neural activity throughout the auditory pathway is sensitive to a variety of statistical cues such as the sound contrast, modulation power spectrum, and correlation structure (4–12), how sound summary statistics contribute toward basic auditory tasks such as recognition and discrimination of sounds is poorly understood. Furthermore, it is unclear where along the auditory pathway summary statistics are represented and how they are reflected in neural activity. The inferior colliculus (IC) is one candidate midlevel structure for representing such summary statistics. As the principal midbrain auditory nucleus, the IC receives highly convergent brainstem inputs with varied sound selectivities. Neurons in the IC are selective over most of the perceptually relevant range of sound modulations and neural activity is strongly driven by multiple high-order sound statistics (4–7, 10). In previous work, we showed the correlation statistics of natural sounds are highly informative about stimulus identity and they appear to be represented in the correlation statistics of auditory midbrain neuron ensembles (4). Correlations between neurons have also been proposed as mechanisms for pitch identification (13) and sound localization (14). This broadly supports the hypotheses that high-order sound statistics are reflected in the response statistics of neural ensembles and that these neural response statistics could potentially subserve basic auditory tasks.Here using natural and synthetic texture sounds, we test the hypothesis that statistical structure in natural texture sounds modulates the response statistics of neural ensembles in the IC of unanesthetized rabbits, and that distinct neural response statistics have the potential to contribute toward sound recognition and discrimination behaviors. By comparing the performance of neural decoders with human texture perception, we find that place rate representation of sounds (neural spectrum) accumulates evidence about the sounds on relatively fast time scales (tens of milliseconds) exhibiting decoding trends that mirror those seen for human texture discrimination. High-order statistical sound cues, by comparison, are reflected in the correlation statistics of neural ensembles, which require substantially longer evidence accumulation times (>500 ms) and follow trends that mirror those measured for human texture recognition. Collectively, the findings suggest that spectrum cues and accompanying place rate representation (neural spectrum) may contribute surprisingly little toward the recognition of auditory textures. Instead, high-order statistical sound structure is reflected in the distributed patterns of correlated activity across IC neural ensembles and such neural response structure has the potential to contribute toward the recognition of natural auditory textures.