Saksenaea species are a rare cause of mucormycosis, the majority associated with cutaneous and subcutaneous infections resulting from trauma in both immunocompromised and immunocompetent individuals. Unlike other causative agents of mucormycosis, cerebral infections are exceptionally rare. We describe the first case of isolated cerebral infection by Saksenaea in a 4-year-old previously healthy male child who presented with headaches. He had no past medical history other than an episode of febrile seizures. In addition to raising the awareness of an unusual presentation of infection by Saksenaea, this case highlights the importance of pathologic examination for the prompt diagnosis of mucormycosis as well as the specific fungal identification for treatment as Saksenaea spp. may be more susceptible to posaconazole and less susceptible to amphotericin B compared to more common causes of mucormycosis.
Intracerebral hemorrhage (ICH) is a significant cause of morbidity and mortality worldwide. Hypertension and cerebral amyloid angiopathy (CAA) are the most common causes of primary ICH, but the mechanism of hemorrhage in both conditions is unclear. Although fibrinoid necrosis and Charcot-Bouchard aneurysms (CBAs) have been postulated to underlie vessel rupture in ICH, the role and significance of CBAs in ICH has been controversial. First described as the source of bleeding in hypertensive hemorrhage, they are also one of the CAA-associated microangiopathies along with fibrinoid necrosis, fibrosis and "lumen within a lumen appearance." We describe clinicopathologic findings of CBAs found in 12 patients out of over 2700 routine autopsies at a tertiary academic medical center. CBAs were rare and predominantly seen in elderly individuals, many of whom had multiple systemic and cerebrovascular comorbidities including hypertension, myocardial and cerebral infarcts, and CAA. Only one of the 12 subjects with CBAs had a large ICH, and the etiology underlying the hemorrhage was likely multifactorial. Two CBAs in the basal ganglia demonstrated associated microhemorrhages, while three demonstrated infarcts in the vicinity. CBAs may not be a significant cause of ICH but are a manifestation of severe cerebral small vessel disease including both hypertensive arteriopathy and CAA.
Coronavirus disease 2019 (COVID-19) is emerging as the greatest public health crisis in the early 21stcentury. Its causative agent, Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), is an enveloped single stranded positive-sense ribonucleic acid virus that enters cells via the angiotensin converting enzyme 2 receptor or several other receptors. While COVID-19 primarily affects the respiratory system, other organs including the brain can be involved. In Western clinical studies, relatively mild neurological dysfunction such as anosmia and dysgeusia is frequent (~70-84%) while severe neurologic disorders such as stroke (~1-6%) and meningoencephalitis are less common. It is unclear how much SARS-CoV-2 infection contributes to the incidence of stroke given co-morbidities in the affected patient population. Rarely, clinically-defined cases of acute disseminated encephalomyelitis, Guillain-Barre syndrome and acute necrotizing encephalopathy have been reported in COVID-19 patients. Common neuropathological findings in the 184 patients reviewed include microglial activation (42.9%) with microglial nodules in a subset (33.3%), lymphoid inflammation (37.5%), acute hypoxic-ischemic changes (29.9%), astrogliosis (27.7%), acute/subacute brain infarcts (21.2%), spontaneous hemorrhage (15.8%), and microthrombi (15.2%). In our institutional cases, we also note occasional anterior pituitary infarcts. COVID-19 coagulopathy, sepsis, and acute respiratory distress likely contribute to a number of these findings. When present, central nervous system lymphoid inflammation is often minimal to mild, is detected best by immunohistochemistry and, in one study, indistinguishable from control sepsis cases. Some cases evince microglial nodules or neuronophagy, strongly supporting viral meningoencephalitis, with a proclivity for involvement of the medulla oblongata. The virus is detectable by reverse transcriptase polymerase chain reaction, immunohistochemistry, or electron microscopy in human cerebrum, cerebellum, cranial nerves, olfactory bulb, as well as in the olfactory epithelium; neurons and endothelium can also be infected. Review of the extant cases has limitations including selection bias and limited clinical information in some cases. Much remains to be learned about the effects of direct viral infection of brain cells and whether SARS-CoV-2 persists long-term contributing to chronic symptomatology.
As Artificial Intelligence (AI) making advancements in medical decision-making, there is a growing need to ensure doctors develop appropriate reliance on AI to avoid adverse outcomes. However, existing methods in enabling appropriate AI reliance might encounter challenges while being applied in the medical domain. With this regard, this work employs and provides the validation of an alternative approach -- majority voting -- to facilitate appropriate reliance on AI in medical decision-making. This is achieved by a multi-institutional user study involving 32 medical professionals with various backgrounds, focusing on the pathology task of visually detecting a pattern, mitoses, in tumor images. Here, the majority voting process was conducted by synthesizing decisions under AI assistance from a group of pathology doctors (pathologists). Two metrics were used to evaluate the appropriateness of AI reliance: Relative AI Reliance (RAIR) and Relative Self-Reliance (RSR). Results showed that even with groups of three pathologists, majority-voted decisions significantly increased both RAIR and RSR -- by approximately 9% and 31%, respectively -- compared to decisions made by one pathologist collaborating with AI. This increased appropriateness resulted in better precision and recall in the detection of mitoses. While our study is centered on pathology, we believe these insights can be extended to general high-stakes decision-making processes involving similar visual tasks.
Recent developments in AI have provided assisting tools to support pathologists’ diagnoses. However, it remains challenging to incorporate such tools into pathologists’ practice; one main concern is AI’s insufficient workflow integration with medical decisions. We observed pathologists’ examination and discovered that the main hindering factor to integrate AI is its incompatibility with pathologists’ workflow. To bridge the gap between pathologists and AI, we developed a human-AI collaborative diagnosis tool— xPath —that shares a similar examination process to that of pathologists, which can improve AI’s integration into their routine examination. The viability of xPath is confirmed by a technical evaluation and work sessions with 12 medical professionals in pathology. This work identifies and addresses the challenge of incorporating AI models into pathology, which can offer first-hand knowledge about how HCI researchers can work with medical professionals side-by-side to bring technological advances to medical tasks towards practical applications.
As Artificial Intelligence (AI) making advancements in medical decision-making, there is a growing need to ensure doctors develop appropriate reliance on AI to avoid adverse outcomes. However, existing methods in enabling appropriate AI reliance might encounter challenges while being applied in the medical domain. With this regard, this work employs and provides the validation of an alternative approach – majority voting – to facilitate appropriate reliance on AI in medical decision-making. This is achieved by a multi-institutional user study involving 32 medical professionals with various backgrounds, focusing on the pathology task of visually detecting a pattern, mitoses, in tumor images. Here, the majority voting process was conducted by synthesizing decisions under AI assistance from a group of pathology doctors (pathologists). Two metrics were used to evaluate the appropriateness of AI reliance: Relative AI Reliance (RAIR) and Relative Self-Reliance (RSR). Results showed that even with groups of three pathologists, majority-voted decisions significantly increased both RAIR and RSR – by approximately 9% and 31%, respectively – compared to decisions made by one pathologist collaborating with AI. This increased appropriateness resulted in better precision and recall in the detection of mitoses. While our study is centered on pathology, we believe these insights can be extended to general high-stakes decision-making processes involving similar visual tasks.
Abstract Hexosamine biosynthesis pathway (HBP) mediated by a series of enzymes is highly upregulated in many cancers to promote the glycosylation of proteins and lipids, while which enzyme in this pathway is better to serve as anti-tumor target is unclear. Here, we found targeting GFAT1, the initial and rate-limiting enzyme in HBP, fails to inhibit the growth of glioblastoma (GBM), the most lethal brain tumor, due to this cancer dramatically elevating NAGK-mediated hexosamine salvage pathway. In contrast, targeting PGM3, the latter enzyme in HBP, blocks both de novo hexosamine synthesis and salvage pathways, effectively inhibiting GBM growth. We further identified HBP enzymes are upregulated by SREBP-1, a key lipogenic transcriptional factor. In turn, this upregulation promotes SREBP-1 activation to stimulate lipogenesis via stabilization of its key transporter SCAP by N-glycosylation modification. Together, our study uncovered a previously unrecognized HBP-SCAP-SREBP-1 feedforward loop and demonstrated targeting PGM3 is an effective approach to treat GBM.
