Although the in situ immunotherapy approach is attractive, there are several disadvantages associated with the intratumoral injection of immunostimulants. This approach is generally limited to those accessible tumors, and becomes even more challenging if repeated injections are needed. Structurally, cGAMP contains the phosphodiester bond that is susceptible to degradation by extracellular phosphodiesterase, also the two phosphodiester bonds in cGAMP restrict its penetration through the plasma membrane. Thus, in order to achieve adequate biological activity, cGAMP is commonly used at relatively high concentrations. However, excessive intratumoral cGAMP may induce programmed death-ligand 1 (PD-L1) overexpression on tumor cells and increase tumor-infiltrating regulatory T cells (Tregs), resulting in a negative impact on antitumor immunity10,14,15,16. Importantly, several studies have shown that intratumoral immunostimulants generally induce local immune response at the injected site, but have limited effect on distant, uninjected tumor sites, implying that the local approach may be inadequate to elicit systemic immunity, or that the systemic response even if induced, may be rendered inactive when exposed to the immunosuppressive tumor microenvironment (TME) at distant naive tumor sites3,10,17.
Intratumoral injection of immunomodulators to elicit anticancer immunity has shown promising results in preclinical studies and is being investigated extensively in clinical trials. Although we also demonstrated the utility of intratumoral injection of NP-cGAMP, we were not intending to compare it with the inhalation approach in this study, because the individuals who would receive this therapy come from different patient populations. In the context of clinical cancer development and treatment strategy, primary cancer, for example, breast cancer or melanoma, is commonly resected by surgery or locally treated once detected. However, lung metastases may develop years later in these patients, and in many cases when the primary cancer is well controlled. We are specifically considering the potential of our inhalation approach for treating this population of cancer patients. From a clinical perspective, these patients have the highest mortality, thus development of effective treatment, for example, immunotherapy, is the most needed.
Given that lung is an extremely common site for breast cancer and melanoma to metastasize50,51 and in many cases multiple lesions develop at peripheral lung52,53, the inhalation approach, which has advantages including its non-invasiveness, feasibility for repeated procedures, and accessibility to multiple lung lesions/lobes at the same time, may have particular translational relevance to deliver immunomodulators to these lung metastases. With a similar strategy of using hypofractionated stereotactic body radiation therapy to treat a single lesion or few lesions in a lung54, and combined with NP-cGAMP inhalation to activate antitumor immunity in both irradiated and non-irradiated tumors, we believe that this nano-immunotherapy system may have the potential for treating lung metastases arising from a variety of primary cancer types.
How do I know what to put in my Report The Project Reporting module in Research.gov can be accessed only by the PI and co-PIs of an award. The module is organized with tabs for each of the components of your report: Accomplishments, Products, Participants, Impact, Changes/Problems, and Special Requirements. The template also allows you to attach PDF documents for images, charts and other supplemental materials; PDF attachments are not permitted for the narrative content of the report. A \"Getting Started Guide\" for creating (and editing) annual and final reports is available at: _general.pdf. (The reporting module can also be used to submit interim reports.)
What if my award was for a workshop The PI is responsible for the report. The Accomplishments section should include: (1) a description of participant selection; (2) a list of persons for whom travel funds were provided (including institutional affiliation and sum awarded); and (3) information about the meeting-including attendance, total number of U.S. participants, and other countries represented, highlights of the program and its outcomes and products.
What if I have other questions The Award and Administration Guide (Ch. II: Grant Administration) has more details on reporting requirements ( _summ.jspods_key=aag). Frequently asked questions about the project outcomes report for the general public can be found on the Policy Office website ( _summ.jspods_key=porfaqs). Workshop reporting requirements are described in FL 26 ( _113.pdf). And, as always, you are encouraged to contact your Program Officer.
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These licensed components are quite valuable to their designers, though. As such they'll want to protect them from being reverse engineered and cloned to be used for free by pirates. As such, the IEEE developed P1735, a standard for encrypting hardware designs to keep them confidential throughout the manufacturing process. This requires you use P1735-compliant engineering software to import the ciphered blocks and integrate them with your own logic before taping out your chip.
The main flaw lies in the standard's use of AES-CBC mode, the bedrock of its encryption system. Because the standard makes no recommendation for any specific padding scheme, developers of P1735-compliant engineering tools often pick the wrong scheme. This makes it possible to decrypt the blueprints without the necessary key using a classic padding-oracle technique.
It's one thing to prevent miscreants from decrypting the licensed schematics or hardware design code; it's another to prevent them from silently modifying bits and bytes to maliciously change the operation of the actual resulting chip. P1735 ought to prevent that, but doesn't.
Over enough iterations, an attacker, with access to an encrypted design and the necessary engineering toolchain, can eventually work out what's what inside the ciphered blueprint from the syntax errors, and alter it to add an invisible backdoor hidden in the physical circuits of the chip. The IEEE's P1735 approach does not do enough to prevent this.
PS: P1735 uses one-time session keys and public-private key pairs to encrypt designs in transit and at rest. However, how the standard keeps a blueprint out of the hands of a miscreant once the design is decrypted in memory by the engineering toolchain for processing is unclear....
Four different binary classification models were constructed using each machine learning algorithm to classify the baseline and other pain intensities (Baseline [BL] vs Pain Level [PL] 1, BL vs PL2, BL vs PL3, and BL vs PL4). Our models achieved higher accuracy for the first 3 pain models than the BioVid paper approach despite the challenges in analyzing real patient data. For BL vs PL1, BL vs PL2, and BL vs PL4, the highest prediction accuracies were achieved when using a random forest classifier (86.0, 70.0, and 61.5, respectively). For BL vs PL3, we achieved an accuracy of 72.1 using a k-nearest-neighbor classifier.
Pain assessment research only using EDA data is limited. Eriksson et al  and Munsters et al  validated the relationship between EDA and pain for newborn infants, suitable for automated pain assessment due to their inability to communicate. By monitoring the EDA data during routine blood sampling or care intervention, they found EDA can differentiate between pain and no pain; however, more research is needed to achieve a clinical-grade level. Manivannan et al  verified whether the EDA could be used as a valid pain indicator for hypnotic analgesia with 10 participants. They used an iron disk to create mechanical pain in a laboratory setup. The experimental results show a clear relation between pain scores and EDA. None of these mentioned works used machine learning algorithms to create a classification model for pain assessment. Furthermore, their dataset includes healthy patients with various stimulus methods to cause pain. In another work, Susam et al  attempted to assess postoperative pain using EDA through a machine learning model. Their model could distinguish between clinical moderate-to-severe pain and no-pain conditions. However, their work only focused on children as a population.
In the work by Werner et al , there were 5 different pain levels, including the baseline level. To properly compare our pain assessment algorithm with their work, we down-sampled our 11 classes to 5 classes. The key factor in this down-sampling is to ensure that the distribution of the labels is as balanced as possible. As a result, we considered pain levels 1-3 as new pain level 1 (PL1), pain level 4 as new pain level 2 (PL2), pain levels 5-7 as new pain level 3 (PL3), and pain levels 8-10 as new pain level 4 (PL4). Based on Table 2, there are only 37 data points for the baseline. To increase the number of samples for the baseline to make our labels more balanced, we up-sampled PL0 based on the reported PL0 data by the patients. We ensured these new baseline data were close enough to the reported pain level 0 labels (less than 10 seconds difference) and had no overlap with other labels. These assumptions were made to make sure (1) we were not reproducing any data and (2) the patients had the same pain level 0 for these new timestamps. By doing this procedure for all the participants, our number of samples for pain level 0 increased from 37 to 86. 59ce067264