Accepted

The periaqueductal gray (PAG) represents a key target of projection neurons residing in the spinal dorsal horn. In comparison to lamina I spinoparabrachial neurons, little is known about the intrinsic and synaptic properties governing the firing of spino-PAG neurons, or whether such activity is modulated by neonatal injury. Here we address this issue using ex vivo whole-cell patch clamp recordings from lamina I spino-PAG neurons in adult male and female FVB mice following hindpaw incision at postnatal day (P)3. Spino-PAG neurons were classified as high output (HO), medium output (MO) or low output (LO) based on their action potential (AP) discharge following dorsal root stimulation. The HO subgroup exhibited prevalent spontaneous burst-firing and displayed initial burst or tonic patterns of intrinsic firing, while LO neurons showed little spontaneous activity. Interestingly, the level of dorsal root-evoked firing significantly correlated with the resting potential and membrane resistance, but not with the strength of primary afferent-mediated glutamatergic drive. Neonatal incision failed to alter the pattern of monosynaptic sensory input, with the majority of spino-PAG neurons receiving direct connections from low-threshold C-fibers. Furthermore, primary afferent-evoked glutamatergic input and AP discharge in adult spino-PAG neurons were unaltered by neonatal surgical injury. Finally, Hebbian long-term potentiation at sensory synapses, which significantly increased afferent-evoked firing, was similar between P3-incised and naïve littermates. Collectively, these data suggest that the functional response of lamina I spino-PAG neurons to sensory input is largely governed by their intrinsic membrane properties and appears resistant to the persistent influence of neonatal tissue damage.

The pathophysiology of interstitial cystitis/bladder pain syndrome (IC/BPS) is multifactorial. Identifying the clinical characteristics and cystoscopic findings of bladder-centered IC/BPS facilitates optimal treatment strategies targeting the diseased urinary bladder. Patients with Hunner's lesion (HIC) and without Hunner's lesion (NHIC) should be treated differently. Based on the histopathological findings, NHIC can be treated with intravesical instillation of urothelial protective agents, such as hyaluronic acid, to cover the urothelial defects. In non-responders, chronic inflammation and higher urothelial dysfunction can be treated with intravesical botulinum toxin A injection, platelet-rich plasma injection, or low-energy shock wave treatment to reduce inflammation, increase tissue regeneration, and improve the urothelial barrier. Patients with HIC should be treated with electrocauterization first; augmentation enterocystoplasty should only be used in end-stage HIC when the contracted bladder is refractory to other treatments. The antiviral agent, valacyclovir, can be used in patients with HIC, small bladder capacity, and high-grade glomerulations. In addition, behavioral modification is always recommended from the beginning of treatment. Treatment with cognitive behavioral therapy interventions in combination with bladder therapy can reduce anxiety and improve treatment outcomes. Herein, recent advances in the pathophysiology and novel treatments for IC/BPS are reviewed.

Chronic musculoskeletal pain (CMP) is a preference-sensitive condition for which numerous treatment options are available, each with benefits and risks. Thus, patient preferences play a critical role in decision-making. This study summarized evidence from discrete choice experiments (DCEs) to quantify patient preferences for CMP treatment and identified important treatment attributes. A systematic review of DCEs on patient preferences for CMP treatment was conducted. Studies were included if they utilized DCE to determine patient preferences for CMP. A previously described methodological assessment tool was used to assess the risk of bias. The treatment attributes were summarized and sorted according to the frequency of citation and relative weight. Subgroup analyses were conducted to explore the intervention-specific attributes. A total of 15 eligible studies with 4065 participants were included. We identified "capacity to realize daily life activities", "risk of adverse events", "effectiveness in pain reduction" and "out-of-pocket cost" as important attributes. Although "treatment frequency" and "onset of treatment efficacy" were less frequently mentioned, they were also important attributes. The attribute of "risk of adverse events" was especially important for drug treatment. The "out-of-pocket cost" and "treatment location and mode" were important attributes of exercise therapy. The attributes identified in this review will inform the design of future DCE studies, facilitate the translation of measurement-based care to value-based care, and provide the rationale to promote shared decision-making and patient-centered care.

Artificial Intelligence in healthcare is growing quickly in diagnostics and treatment management. Despite the quantity and variety of studies its role in clinical care is not clear. To identify the evidence gaps and characteristics of the Artificial Intelligence and Machine Learning techniques in predicting/diagnosing the pain? Pubmed/Embase were searched from the inception to October 2021 for articles without any language restrictions specifically addressing the following: the accuracy of AI in pain considering Brain Imaging, Patient-reported measures, and Electrophysiology, the ability of AI to differentiate stratify severity/types of pain, the ability of AI to predict pain and lastly the most accurate AI technique for given inputs. All the included studies were on humans. Eight hundred forty abstracts were reviewed, and 23 articles were finally included. Identified records were independently screened and relevant data was extracted. We performed conceptual synthesis by grouping the studies using available concepts of AL/ML techniques in diagnosing pain. Then we summarized the number of features/physiological measurements. Structured tabulation synthesis was used to show patterns predictions along with a narrative commentary. A total of 23 articles, published between 2015 and 2020 from 12 countries were included. Most studies were experimental in design. The most common design was cross sectional. Chronic or acute pains were predicted more often. Compared to control, the pain prediction was in the range of 57%-96% by AI techniques. Support Vector Machine and deep learning showed higher accuracy for classifying pain. From this study, it can be inferred that AI/ML can be used to differentiate healthy controls from patients. It can also facilitate categorizing them into new and different clinical subgroups. Lastly, it can predict future pain. The limitations are with respect to studies done after the search period. AL/ ML has a supportive role in pain diagnostics.

Although neurobiological research has shown that interoception plays a role in the perception of pain and its chronification, the relationship between interoceptive sensitivity and pain has not been definitively confirmed by clinical studies. The aim of this study was therefore to better understand the relationship between interoceptive sensitivity, somatization, and clinical pain, and to identify any differences in the interoceptive sensitivity of patients with recurrent vs. chronic pain.